Container terminal yard as the hub of the operation area on both land and sea sides, the space allocation will directly affect the overall operation efficiency of the terminal. In order to reasonably allocate the space of U-shaped yard to improve the efficiency of container loading, two blocks sharing one intelligent guided vehicle(IGV) lane were formed into a group. From the perspective of reducing lane congestion, considering the constraints of high and low workloads of adjacent operation areas within the block groups, and with the goal of minimizing the total loading operation time and balancing the number of containers in the block groups, the first stage of the export container allocation model was constructed, and an immunity compensation mechanism and Metropolis criterion were introduced. And an improved adaptive genetic algorithm was designed by introducing immune compensation mechanism and Metropolis criterion. Based on the allocation results of the first stage, a priority descending balanced stockpiling strategy was established to ensure the continuous operation of the yard crane, and the second stage of the container allocation model was constructed, and a heuristic algorithm was designed to solve the problem. The design of large-scale case experiments shows that the algorithm is able to give effective export container stockpiling area delineation and container space allocation scheme, and the performance of the algorithm is verified by different scale comparison experiments, which shows that the algorithm has a faster convergence speed and is able to optimize 10% of the target value, which improves the overall operational efficiency of the U-shaped automated terminal.