The matching optimization design of the blade geometry of the existing hydraulic model of the impeller and guide vane is carried out in order to improve the hydraulic performance of the waterjet pump. The Latin Hypercube sampling method is used for the space sampling of parameters that affect the performance of the waterjet pumps, such as the impeller blade load, the guide vane blade load and the stacking position. The performance parameters of the pump is obtained through steady numerical simulation of each sample by using the RANS equations. The mapping response model is built between the pump design parameters and the calculated performance parameters based on the artificial neural network. The genetic algorithm is adopted to optimize the waterjet pump hydraulic model with the aim of the maximum hydraulic efficiency. The iterative cycling of the optimization process is automatically completed, which can shorten the design and development period of the hydraulic model. The results show that after optimization, the internal flow field is significantly improved with the same head of the pump, and the efficiency of the calculation design point reaches 91.8%, while the efficiency validated by model test reaches 88.9%, as well as the range of the flow rate in high efficiency region is widened by 15%.