This paper addresses the Perishable Food Vehicle Routing Problem with Time Windows (PF-VRPTW), where product quality deterioration during distribution presents a critical operational challenge. A heuristic-enhanced NSGA-II algorithm balances competing objectives of minimizing transportation costs and preserving food quality through three specialized initialization heuristics—Modified Solomon’s I1, Earliest Deadline First, and Nearest Neighbor—integrated with adaptive genetic operators for perishable product routing. Computational experiments across 15 instances demonstrate the algorithm significantly outperforms standard NSGA-II and weighted-sum genetic approaches, achieving hypervolume improvements of 42.4% while reducing computation time by 39.8% for large instances. Ablation studies confirm domain-specific heuristics contribute substantially to solution quality, with Modified Solomon’s I1 providing the greatest benefit at 25.2% hyper-volume improvement. The algorithm generates diverse Pareto-optimal solutions representing different operational strategies, from cost-focused configurations with fewer vehicles to quality-prioritizing solutions with shorter delivery times. This work advances multi-objective optimization for perishable food logistics by effectively capturing the trade-off between economic efficiency and product freshness.