Abstract: With the widespread popularity of electric vehicles and surging demand, charging pile has attracted wide attention as the main charging equipment of electric vehicles. At the same time, the safety and reliability of charging piles have become the key issues in the development process of electric vehicles. It is very important to carry out the necessary testing of charging piles before they leave the factory.However, the testing of charging piles takes a long time, which seriously affects the testing efficiency and delivery testing time of charging piles. In order to improve the efficiency of charging pile detection, this paper set the minimum completion time of charging pile detection as the objective function, and established the mathematical model of charging pile detection scheduling optimization. In the meantime, in order to overcome the limitations of the classical genetic algorithm, a double mutation operator based on process and equipment variation is designed to maximize population diversity and proposes an improved genetic algorithm with initialization factor and elite strategy. Experimental results show that using the improved genetic algorithm proposed in this paper, the total detection time can be reduced by 33.26% compared with manual detection and 14.84% compared with the classical genetic algorithm, which significantly improves the detection efficiency of charging pile.