In modern corporate logistics transportation, effectively allocating railway platform freight space and optimizing loading route planning are vital for improving transportation performance and minimizing logistics costs. These aspects must be jointly optimized considering fluctuating time-sensitive preferences and the dynamic availability of resources. This dual objective poses a complex multi-objective optimization challenge, as achieving efficiency while reducing costs involves a non-convex, non-differentiable optimization space under real-time constraints. Existing algorithms often fall short of addressing these challenges with sufficient efficiency. In this paper, we develop a comprehensive mathematical framework for the Freight Space Allocation and Railcar Pickup-Delivery Route Planning problem (FSA-PDP). The model aims to enhance transportation efficiency while simultaneously reducing costs. To solve this, we employ a novel multi-objective reinforcement learning (MORL) algorithm. This innovative algorithm is designed to learn a general policy adaptable across diverse railway platform settings and varying preference spaces. By doing so, it effectively balances transportation performance and cost efficiency. Our methodology is validated through extensive experiments utilizing a real-world dataset derived from the large-scale Salt Lake chemical industry. The findings underscore the superior performance and adaptability of MORL, demonstrating its capacity to outperform methods in railway freight logistics.