An electric vehicle is a moving load, the charging process of which is random with respect to spatial and temporal distribution. Disordered charging of large-scale electric vehicles will affect the stability of the grid. In this article the Monte Carlo method is used to simulate the regular mode of disordered charging of large-scale electric vehicles, and then the load curves of electric vehicles charging under different permeability conditions are analyzed. Taking the IEEE33 node system as an example, in this article the forward and backward power flow algorithm is used to calculate the impact of random access of electric vehicles to the distribution system under different permeabilities. For the benefit of the users and the security of the power grid, to realize the “peak clipping and valley filling” of an electric vehicle charging load and the optimization goal of the lowest user charging fee, it is very important that we establish a multiobjective optimization model. A genetic algorithm is used to solve the optimization of the multiobjective model. The simulation results show that the model is practical and the algorithm is effective.