The ecological management of municipal solid waste has become a strategic priority for sustainable urban development. Although many Electric Vehicle Routing Problem (EVRP) studies primarily focus on travel distance, the progressive variation of vehicle payload during collection operations can substantially affect electricity consumption and associated environmental impacts. This study aims to evaluate an eco-efficient EVRP framework that explicitly incorporates payload-dependent energy consumption and to investigate how payload sensitivity influences route evaluation and metaheuristic performance. The proposed model jointly considers travel distance, battery capacity, payload-sensitive energy consumption, and indirect CO₂ emissions. A comparative computational analysis was conducted using the Schneider benchmark dataset, where Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA) were implemented under the same routing formulation and evaluated through a sensitivity analysis of the payload-sensitivity coefficient γ. The results demonstrate that incorporating payload-dependent energy consumption significantly changes the environmental assessment of routing solutions. The sensitivity analysis revealed a consistent distance–energy–emission trade-off. For GA, increasing γ from 0.00 to 0.10 reduced the average travelled distance from 1178.63 km to 1166.39 km, while energy consumption increased from 2180.47 to 2238.86 kWh and estimated emissions increased from 1565.58 to 1607.50 kg CO₂e. These findings indicate that shorter routes are not necessarily the most energy-efficient when payload effects are considered. GA exhibited the best overall performance across all scenarios, whereas PSO remained very competitive under moderate payload sensitivity and SA produced feasible but less competitive solutions. The study is limited to benchmark instances and a simplified payload-sensitive energy formulation. Nevertheless, the proposed framework provides a practical basis for integrating environmental considerations into electric waste collection planning. The originality of this work lies in explicitly quantifying how payload sensitivity affects energy consumption, CO₂ emissions, and algorithmic performance within a unified EVRP framework.