This thesis contributes to the debate on how uncertainty and concepts of sustainable development can be put into modern supply chain network and focuses on issues associated with the design of multi-criteria supply chain network under uncertainty. First, we study the literature review , which is a review of the current state of the art of Supply Chain Network Design approaches and resolution methods. Second, we propose a new methodology for multi-criteria Supply Chain Network Design (SCND) as well as its application to real Supply Chain Network (SCN), in order to satisfy the customers demand and respect the environmental, social, legislative, and economical requirements. The methodology consists of two different steps. In the first step, we use Geographic Information System (GIS) and Analytic Hierarchy Process (AHP) to buildthe model. Then, in the second step, we establish the optimal supply chain network using Mixed Integer Linear Programming model (MILP). Third, we extend the MILP to a multi-objective optimization model that captures a compromisebetween the total cost and the environment influence. We use Goal Programming approach seeking to reach the goals placed by Decision Maker. After that, we develop a novel heuristic solution method based on decomposition technique, to solve large scale supply chain network design problems that we failed to solve using exact methods. The heuristic method is tested on real case instances and numerical comparisons show that our heuristic yield high quality solutions in very limited CPU time. Finally, again, we extend the MILP model presented before where we assume that the costumer demands are uncertain. We use two-stage stochastic programming approach to model the supply chain network under demand uncertainty. Then, we address uncertainty in all SC parameters: opening costs, production costs, storage costs and customers demands. We use possibilistic linear programming approach to model the problem and we validate both approaches in a large application case.