With the rapid development of e-commerce and on-demand logistics, distribution centers are confronted with the dual challenge of surging cargo volumes and insufficient loading efficiency. In scenarios involving the parallel transportation of multiple vehicles, loading decisions across different vehicles exhibit mutual coupling characteristics. Without coordinated global planning, such unregulated decision-making not only diminishes the overall space utilization rate of the fleet but also gives rise to risks of uneven load distribution and compromised transportation stability. Existing research in this field, however, remains primarily focused on single-vehicle loading optimization, a limitation that renders it unable to meet the practical operational demands of modern logistics systems. To address these issues, this paper investigates the three-dimensional cargo loading problem from a multi-vehicle collaborative perspective and proposes a multi-agent reinforcement learning (RL) method based on Value Decomposition Networks (VDN), which is designed to realize the joint optimization of transportation efficiency and vehicle operational safety. Specifically, the paper models the multi-vehicle loading process as a Partially Observable Optimal Multi-Agent Markov Decision Process (OPOMDP). By adopting a centralized training and distributed execution framework, each vehicle is enabled to generate a globally coordinated loading strategy solely on the basis of its local observation data. On this foundational framework, two key mechanisms are devised to enhance the performance of the loading strategy. First, a stability cost metric, which is constructed based on dynamic shifts in the vehicle’s center of gravity, is introduced to quantify the rollover risk of the vehicle; this metric is then integrated into the reward signal of the RL model, thereby guiding the strategy to maintain transportation safety while improving the cargo loading rate. Second, a specialized placement reward mechanism is proposed to incentivize each agent to proactively explore optimal cargo rotation methods and compact spatial packing structures, which in turn reduces intra-layer voids and elevates the overall volume utilization rate of the cargo compartment. Experimental results demonstrate that the proposed method outperforms traditional heuristic algorithms and mainstream RL baseline algorithms by a significant margin in terms of loading success rate, volume utilization rate, and weight utilization rate. Meanwhile, the stability constraint mechanism embedded in the model effectively mitigates the vehicle rollover risk, ultimately achieving the synergistic optimization of loading efficiency and transportation safety.