In recent years, the increase in Gross Domestic Product (GDP) and global trade has significantly expanded the role of freight transport, particularly maritime transport. Container trade has experienced notable growth, and shipping lines have become one of the most important container transport methods. Proper scheduling of these shipping lines requires precise planning, with factors such as port service availability playing a crucial role. The term “port time window” refers to specific timeframes during which a port can provide services to ships, significantly affecting shipping line schedules. Well-designed schedules not only affect fuel consumption but also contribute to reducing air pollution. However, uncertainty in various parameters can degrade the quality of scheduling outcomes. The present research proposes a scheduling model for container shipping lines within a green supply chain. It aims to minimize transportation costs, fuel consumption, and environmental pollution while considering port time windows and demand uncertainty. Given the complexity and constraints of the problem, it is classified as NP-hard. Small-scale instances are solved using GAMS software, while metaheuristic algorithms are applied to large-scale problems. Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are employed for comparison, and results are analyzed regarding solution time and accuracy.