In order to obtain reasonable parameters for the serpentine liquid-cooled structure of the power battery pack, an optimal design method for the liquid-cooled structure of the power battery pack combining the uniform design method, BP neural network algorithm and multi-objective genetic algorithm is proposed. Firstly, a single cell temperature rise test is carried out to verify the cell simulation calculation model and provide support for the data accuracy of uniform design test and parameter processing. Then it was determined that the temperature difference of the battery pack and the pressure drop of the liquid cooling structure were the design objectives, and the coolant inlet mass flow rate, coolant inlet diameter and liquid cooling tube pipe width were the design parameters. CFD simulation was conducted through uniform design test to obtain the specific parameters of the liquid cooling structure, and the agent model between the design objectives and the design parameters was obtained by training with BP neural network algorithm. Finally, the NGSA-II multi-objective genetic algorithm is used to calculate the proxy model to obtain the Pareto solution set, and the optimal Pareto solution is selected according to the engineering experience to verify the optimization results and compare the simulation results before and after optimization. The comparison results before and after optimization show that: the maximum temperature of the battery pack is reduced by 5.06℃, with a decrease of 14.3%; the maximum temperature difference of the battery pack is reduced by 4.88℃, with a decrease of 51.5% compared with that before optimization; the pressure drop of the liquid cooling structure is increased by 122.8%, which solves the negative pressure problem and reduces the coolant pressure loss, which verifies the effectiveness of the optimization method.