This paper addresses two significant challenges facing the e-commerce logistics sector: optimizing next-day delivery routing with strict time windows and determining the optimal fleet composition of electric and diesel vehicles. We introduce a mixed-integer linear programming model for the Next-Day Green Hybrid Vehicle Routing Problem with Time Windows (NDGVRP-TW), simultaneously aimed at reducing carbon emissions, enhancing profitability, and improving customer satisfaction. Three fleet configurations—pure electric, pure diesel, and hybrid—are comparatively analyzed to identify the most profitable and environmentally sustainable composition. To effectively address this NP-hard problem, we developed a modified Particle Swarm Optimization algorithm integrated with Simulated Annealing (mPSO-SA). This algorithm uniquely incorporates random reshuffling of personal best positions and perturbation steps of the global best solution, mitigating premature convergence commonly observed in traditional optimization approaches. Computational experiments utilizing real-world data demonstrate that the mPSO-SA algorithm significantly surpasses conventional Genetic Algorithms (GA) and standard Particle Swarm Optimization (PSO), achieving over 25% improvements in convergence speed and solution quality. Our results reveal that hybrid fleets substantially outperform homogeneous fleet configurations, achieving considerable operational advantages including an 18.8% reduction in labor costs, a 26.3% decrease in distance-related expenses, and an impressive 77.7% reduction in carbon emissions compared to pure diesel fleets. Additionally, the hybrid approach notably decreases late-delivery penalties by 28.6% relative to pure electric fleets, effectively balancing the environmental benefits of electric vehicles against their operational limitations, particularly their constrained driving ranges. Our sensitivity analyses generate important managerial insights, emphasizing that fleet managers should strategically align vehicle capacities to prevent route inefficiencies, proactively adapt routing strategies in response to increasing operational costs, and meticulously schedule deliveries to minimize late penalties. Implementing these insights facilitates substantial improvements in profitability, resource optimization, and customer satisfaction compared to conventional benchmarks, providing enterprises with actionable, data-driven strategies for sustainable competitive advantage.