Recently, the rapid increase in electric vehicles (EVs) has significantly raised their integration into the power grid. This overlap between EV charging times and daily routines has created 'peaks upon peaks,' exerting substantial pressure on distribution networks. Therefore, shifting EV charging loads to off-peak periods is essential. The existing time-of-use (TOU) static pricing is no longer suitable for current demands, leading to the introduction of a dynamic pricing mechanism. EV charging demand is highly adjustable yet unpredictable, necessitating the proposal of a compliance incentive coefficient. Based on this, a dual-objective function is established, aiming to minimize charging costs and the grid load peak-valley difference, thereby enhancing the predictability and controllability of the charging load. The existing Particle Swarm Optimization (PSO) algorithm faces issues with global search capability and convergence speed. To address these shortcomings, the Grey Wolf Optimization (GWO) algorithm is proposed. This algorithm can achieve an orderly charging strategy for EVs under dual constraints, effectively reducing user charging costs and minimizing the grid’s peak-valley difference, successfully achieving the goal of peak shaving and valley filling.