A large number of electri c vehicles connected to charging stations will increase the peak-valley difference and affect the safe and stable operation of the electric vehicle charging stations. Thus a coordinated charging strategy for plug-in electric vehicle (PEV) charging stations based on Mind Evolutionary Algorithm (MEA) is proposed. The strategy set minimum customer charging costs and minimum peak-valley difference as objective function. The optimal charging period of electric vehicles is calculated dynamically by using MEA. Customers decide whether or not to respond to peak-valley prices and to delay their charging to lower price periods by themselves. The charging coordination of electric vehicles is then realized. In order to verify the effectiveness of the proposed strategy, the Monte Carlo simulation method was utilized to generate the charging needs of customers based on actual customer charging behaviors. The distribution transformer load profiles, customer charging costs were simulated under uncoordinated and coordinated charging scenarios correspondingly. Simulation results indicate that under the proposed coordinated charging control strategy, customer charging costs can be greatly reduced and the peak shaving of distribution transformer loading profile can also be achieved; compared with genetic algorithm, the effect of MEA is better.