With the rapid increase in the number of electric vehicles, the disordered grid-connected charging of electric vehicles will bring huge uncertainty to the load stability of the power grid. Therefore, it is extremely important to optimize the charging of electric vehicles. In order to solve this problem, a multi-objective electric vehicle charging optimization strategy based on hybrid particle swarm optimization genetic algorithm (HPSOGA) is proposed in this paper. The Monte Carlo method is used to establish a charging load curve based on the travel patterns of car owners. Based on the traditional PSO, the iterative mechanism of GA is introduced, and HPSOGA is formed to solve the multi-objective optimization model established based on the minimum user charging cost and the minimum grid load fluctuation rate. The simulation analysis is carried out in combination with specific cases. The results show that the multi-objective electric vehicle charging optimization strategy based on HPSOGA has a faster optimization speed and a better optimization effect, further decreasing the grid load peak, increasing the grid load valley, effectively reducing the grid load fluctuation rate, and effectively cutting the charging cost of car owners.