With the continuous expansion of global trade and shipping, container throughput at terminals increases exponentially. Higher requirements are posed on port logistics efficiency, particularly the collaborative operation efficiency of automated guided vehicles (AGVs). Existing centralized algorithms and distributed algorithms exhibit limitations in balancing computational complexity and optimization quality. The demands of automated container terminals for shortening makespan and improving efficiency cannot be effectively accommodated by existing algorithms. To address these problems, a Hybrid task Allocation Algorithm (HAA) combining centralized algorithms and distributed bid was proposed. First, a multi-objective optimization model was constructed from the perspectives of the scheduling system, AGV fleet, and task set to minimize makespan, total AGV travel distance, and task execution delay. Second, a genetic algorithm was utilized to solve this problem. The task set was divided into ordered task subsets equal to the number of AGVs. Finally, bid values for the task subsets were distributively computed by each AGV. The cost matrix constructed from the bid values was solved using the Hungarian algorithm. Through simulation experiments, the proposed HAA method was validated under scenarios with various task scales and task densities. Comparative analyses with traditional centralized algorithm and distributed algorithms were performed. The results demonstrate that a solution quality close to that of centralized algorithms is achieved by the proposed method. Meanwhile, the computation time is reduced by 39.09% on average. Good and stable optimization performance is exhibited. Furthermore, the method can be effectively applied to periodic scheduling scenarios.