Aiming at the power problem of Automated Guided Vehicle （AGV） in the process of performing tasks in Automated Container Terminal （ACT）， an integrated scheduling considering AGV charging strategy based on improved Non-dominated Sorting Genetic Algorithm-Ⅱ （NSGA-Ⅱ） was proposed. Firstly， considering the power consumption of AGV under different operating statuses in the integrated scheduling mode of quay crane， yard crane and AGV， a multi-objective mixed programming model with the goal of minimizing the completion time and total power consumption was established. Secondly， to improve the performance of the traditional NSGA-Ⅱ， an adaptive NSGA-Ⅱ was designed and compared with CPLEX solver and Multi-Objective Partical Swarm Optimization （MOPSO） algorithm on performance. Finally， different charging strategies and equipment number ratios of AGV were designed for experimental research. The experimental results of algorithm comparison show that the solution results of the adaptive NSGA-Ⅱ are improved by 2. 80% and 2. 63% respectively on the two objectives proposed compared with NSGA-Ⅱ. The experimental results of applying the adaptive NSGA-Ⅱ to study the ratio of charging strategies and equipment number ratios show that increasing AGV charging number can reduce AGV charging time， and adjusting the ratio of the equipment number to 3：3：9 and 3：7：3 lead to the highest time utilization of yard crane and AGV respectively. It can be seen that the AGV charging strategy and equipment number ratio can influence the terminal integrated scheduling with multiple equipment.