To deal with the scheduling problem in rail vehicle assembly, where assembly line task allocation is complex and car body components require frequent cross-station transfers relying on trolleys, this study proposed an end-to-end hierarchical multi-agent deep reinforcement learning framework for scheduling optimization. Firstly, the allocation of assembly tasks across multiple assembly lines was modeled as a sequential decision problem. The high-level agent encoded the assembly task and line features using a Transformer and generated line assignment strategies with a Pointer Network. Secondly, the lower-level agents coordinated the selection of operations, station assignments, and dolly scheduling, and used Graph Attention Networks to extract relational features from heterogeneous nodes. Finally, multiple comparison experiments were conducted to validate the effectiveness of the proposed method. The results show that the method achieves optimal scheduling across different instance scales. The coordination of low-level agent strategies achieves an average maximum makespan gap of 11.36%, which outperforms the 15.00% achieved by the graph isomorphism network method, and the method provides high-quality scheduling with computation efficiency significantly higher than the Late Acceptance Hill Climbing algorithm. The proposed hierarchical collaborative scheduling framework achieves unified modeling and coordinated optimization of assembly task assignment and multi-resource scheduling, providing an efficient and adaptable intelligent optimization approach for rail vehicle assembly scheduling.