The work aims to propose a multi-objective packing algorithm suitable for large-scale cargo loading to solve the problems of single optimization objective and low optimization efficiency of existing 3D packing algorithms, so as to improve the efficiency of packing planning and ensure the safety of cargo transportation. Firstly, considering five realistic constraints, a multi-objective cargo loading optimization model was established with the volume utilization and the shift of the center of gravity of the loading layout as the optimization objectives. Then, the cargoes were pre-grouped by anthropomorphic packing to reduce the decision space, and the initial solutions were generated by combining the grouping information with the packing algorithm; A data-driven cross operator was introduced to packing to improve the convergence of the algorithm; Multi-strategy mutation operators were designed to improve the diversity of algorithm results. With the public data set and real air cargo data as the experimental data, the experimental results showed that the average volume utilization rate of strongly heterogeneous cargoes in container loading reached 92.0%. The space offset of the center of gravity position was reduced from 20 cm to 7.5 cm. And the running time of the algorithm was reduced by 73.5% under various constraints. Therefore, the algorithm proposed in this paper is applied to solve the large-scale multi-objective three-dimensional packing problem, which improves the packing quality and efficiency, and can provide reference for the engineering application of the three-dimensional packing algorithm.