Abstract: Efficient and accurate grading of Monopterus albus (rice field eel) with different body sizes is a key requirement for automated aquaculture production and standardized processing. To meet the demand for high-throughput and high-precision size classification, a vibration-type rice field eel grading machine integrating a vibration mechanism and a spraying device was designed. The objective of this study was to evaluate the grading performance of the proposed equipment and to optimize its operating parameters through numerical simulation and experimental validation.A coupled dynamic simulation model of the grading machine and rice field eel was established using RecurDyn–EDEM co-simulation technology. The rigid–flexible multibody dynamics of the grading system, including the vibration exciter, grading sieve, and supporting structure, were modeled in RecurDyn, while the discrete element method implemented in EDEM was used to characterize the motion, collision behavior, and contact interactions of individual rice field eels. Appropriate contact models and mechanical parameters were defined to realistically describe eel–eel and eel–machine interactions, and a spraying-assisted lubrication effect was considered to improve material dispersion and reduce excessive friction during grading. Grading efficiency and grading accuracy were selected as the primary performance indices to quantitatively assess the grading process. Based on the coupled simulation framework, single-factor simulations were first conducted to analyze the independent effects of vibration frequency, grading sieve inclination angle, and feeding quantity on grading performance. The results of the single-factor experiments showed that with an increase in the number of rice field eels, the grading efficiency first increased and then decreased, while the grading accuracy gradually decreased. As the inclination angle of the grading sieve increased, the grading efficiency increased, whereas the grading accuracy decreased. With increasing vibration frequency, the grading efficiency increased continuously, while the grading accuracy first increased and then decreased. Subsequently, a multi-factor orthogonal experimental design was applied to identify the relative importance of each parameter and their combined influence on grading efficiency and accuracy. The simulation results were further verified through physical experiments using a prototype grading machine under corresponding operating conditions, allowing for direct comparison between numerical predictions and measured data. The results showed that the grading sieve inclination angle exerted the greatest influence on both grading accuracy and efficiency, followed by feeding quantity and vibration frequency. Improper parameter combinations resulted in reduced grading performance due to insufficient dispersion, material accumulation, or excessive sliding velocity on the sieve surface. To further improve grading performance, parameter optimization was performed using Design-Expert software, with grading efficiency and grading accuracy as the optimization objectives. The optimal parameter combination was determined as a vibration frequency of 30 Hz, a grading sieve inclination angle of 2.6°, and a feeding quantity of 27 individuals. Under these conditions, the experimentally measured grading efficiency reached 0.637 individuals per second, and the average grading accuracy was 86.7%. The relative errors between experimental and simulation results were 7.97% for grading efficiency and 3.58% for grading accuracy, indicating good consistency and predictive capability of the coupled simulation model. The findings demonstrate that RecurDyn–EDEM coupled simulation is an effective tool for analyzing the complex interaction between vibration-driven machinery and elongated aquatic organisms. The optimized grading parameters and validated simulation methodology provide a reliable basis for the structural improvement and performance enhancement of vibration-based rice field eel grading equipment. This research offers practical technical support for the development of intelligent, efficient, and scalable grading systems for aquatic products with similar morphological characteristics.