<p>Automated container terminals often face the problem that the original terminal scheduling plan cannot be implemented successfully due to uncertainties such as equipment failure, power supply interruption, and cargo damage. Decision makers want to get multiple scheduling alternatives according to the current actual situation. In this case, multi-modal optimization is a promising method to find multiple alternative solutions. In this paper, we propose a multi-strategy cooperative multi-modal multi-objective bat algorithm (MC-MMOBA) to obtain multiple terminal scheduling plans with the same objective values. Combining the internal constraints of multiple equipment unloading operations, the scheduling optimization model is constructed with the objectives of minimizing the maximum completion time of the operation and the total energy consumption of the equipment simultaneously. A multi-modal multi-objective bat algorithm is designed to solve the model. Considering the discrete nature of integrated scheduling of automated container terminal, a multi-layer encoding method is designed to transform the continuous solution space of the bat algorithm’s search process into a discrete solution space. Simultaneously improvement strategies, such as tent chaotic mapping, ring topology structure, and stagnation detection, are integrated into the algorithm. The superiority of our proposed algorithm is verified by comparing the experimental results with three well-known multi-objective optimization algorithms on 11 test functions, where the proposed algorithm achieves better Pareto Set Proximity and Hypervolume values on 9 test functions. For terminal scheduling cases of different scales, the proposed algorithm’s Pareto non-dominated solutions dominate those of the comparison algorithms, and it can generate more equivalent scheduling schemeswhile ensuring optimal operation efficiency and energy consumption, which fully validates the effectiveness of the model and the algorithm.</p>