This study presents a multi-period multi-objective mixed-integer linear programming model for sustainable supply chain optimization. The proposed framework integrates economic, environmental, social, and resilience factors into a single optimization problem, ensuring balanced decision-making across multiple dimensions. The model ensures optimal supplier selection, facility operations, transportation planning, and inventory management while addressing cost efficiency, emission reduction, labor opportunities, and supply chain resilience. It incorporates carbon tax penalties, emission constraints, renewable energy thresholds, and backup route activation to enhance sustainability and robustness under disruptions. The proposed framework balances conflicting objectives through a weighted optimization approach while enforcing key capacity, demand fulfillment, and sustainability constraints. The model dynamically activates backup routes based on failure probability thresholds, ensuring reliability in uncertain conditions. Computational results demonstrate improved cost-effectiveness, reduced environmental impact, enhanced social responsibility, and resilient supply chain performance. Numerical experiments demonstrate that the proposed framework effectively achieves optimal trade-offs among economic, environmental, social and resilience objectives, contributing to sustainable decision-making in supply chain optimization.