The increase in container throughput has led to higher fossil energy consumption and pollution emissions at terminals. Various renewable energy sources have been integrated into port energy systems. The electrified quay cranes (QCs) and automated guided vehicles (AGVs) require a significant amount of power from the port energy system. Although there has been some research on multi-energy systems in ports, these studies have not considered the operation processes of QCs and AGVs, which affects the utilization rate of renewable energy. This paper proposes a co-optimization problem of multi-equipment operation and energy in automated container terminals. A multi-objective model is established to minimize the makespan, equipment waiting time, and energy scheduling costs. A multi-population chaotic particle swarm optimization algorithm with a hybrid mutation strategy is designed to solve the problem. Experimental results show that the proposed algorithm outperforms the non-dominated sorting genetic algorithm II and strength pareto evolutionary algorithm II. The co-scheduling solution presented can bring higher economic benefits to ports, improve the utilization rate of renewable energy, and reduce energy demand and carbon emissions.