The Quay Crane Scheduling Problem (QCSP) is essential for optimizing smart port operations, significantly impacting sustainable urban logistics. Effective scheduling enhances cargo handling efficiency, reduces vessel turnaround times, and minimizes port congestion, thus improving the overall productivity of urban transport networks. The QCSP aims to determine the optimal sequence of crane operations to reduce container handling time while addressing constraints such as crane interference and safety distances. This paper presents a genetic algorithm (GA) approach to solve the QCSP, utilizing natural selection-inspired metaheuristic techniques. The GA evolves an initial population of crane schedules through iterative refinement based on a fitness function that evaluates total handling time and critical constraints. A simulation demonstrates the GA’s effectiveness across various scenarios, revealing significant reductions in handling time and showcasing its scalability and adaptability to real-world conditions. The findings suggest broader applications of GA-based scheduling for other urban logistics challenges, contributing to the development of resilient and sustainable smart cities.