Abstract: In order to improve the hydraulic performance of the turbine type energy recovery integrated machine, RBF neural network, NSGA-Ⅱ genetic algorithm and numerical simulation integrated optimization design method were used to optimize the design of high-pressure pump under multiple working conditions. In order to do that, the weighted average efficiency at multiple operation was taken as the optimization objective, the head under the design condition was taken as the constraint condition, and the four parameters, i.e., blade inlet angle, outlet angle, outlet width and wrap were selected for the Plackett-Burman test design, and the optimal Latin hypercube sampling method was used to choose the design point in the design space. Besides, based on the Isight multidisciplinary optimization platform, an intelligent hydraulic optimization platform was built by writing batch processing commands to integrate CFturbo, ICEM, CFX, which can achieve automatic CFD prediction of high-pressure pumps. Based on the numerical simulation results, the nonlinear relationship between the objective function and geometric parameters was established using RBF neural network, and the NSGA-Ⅱ algorithm was used to optimize the model. The results indicate that the RBF neural network model can accurately predict the relationship between the head, efficiency and the design variables. Compared with the initial scheme, the weighted average efficiency of the optimized high-pressure pump at three points is improved by 3.38%, more specifically, the efficiency under 0.8Q_d,1.0Q_d and 1.2Q_d is increased by 2.21%,3.59%, 4.23%, respectively, and the shaft power under the design condition is slightly lower than the prototype model. Compared with the flow velocity distribution, streamlines, turbulence energy distribution of the optimized and reference impeller, it is found that the low-speed area at the impeller inlet is reduced, the internal velocity gradient distribution is more uniform, the flow field is significantly improved, and the hydraulic loss of energy dissipation is reduced after optimization. The optimization design method presented in this paper can provide theoretical basis for the hydraulic optimization design of high-pressure pumps under multiple operating conditions.