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In the contemporary landscape of global logistics, the efficient delivery of commodities at scale has become imperative. Central to this challenge is the Dynamic Pickup and Delivery Problem (DPDP). While DPDP is inherently complex due to stochastic orders and real-time requirements, it becomes particularly formidable in specific supply chain variants constrained by strict loading protocols, such as the Last-In-First-Out (LIFO) requirement found in air cargo and side-loading fleets. Traditional heuristics often struggle to escape local optima under such rigid constraints, while standard Reinforcement Learning (RL) faces difficulties with reward sparsity and high-dimensional action spaces. To address these challenges, we propose Evo-RL, a holistic evolutionary reinforcement learning framework. Unlike disjointed hybrid methods, Evo-RL establishes a synergistic closed loop: the RL agent acts as a strategic planner to capture latent spatiotemporal dependencies and generate feasible initial assignments, while a population-based Genetic Algorithm (GA) serves as an optimization engine to refine sequences and provide dense feedback signals. This collaborative mechanism allows the RL agent to overcome reward sparsity by learning from the diverse evolutionary population. We evaluate our approach on the real-world, large-scale DPDP benchmark from the ICAPS 2021 competition. 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