1. The problem considered The increasing importance of environmental issues has prompted decisionmakers to incorporate environmental factors into supply chain network design (SCND) models. We propose a bi-objective SCND model to minimize two conflicting objectives: the total cost and the environmental impact expressed by CO2 emissions. The logistics network consists of four layers: suppliers, plants, distribution centers (DCs) and customers. The model considers several possible transportations modes in the network, each transportation mode having a lower and upper capacity limitation. Moreover, we consider different candidate technology levels at the plants and DCs. Each technology represents a type of service with associated fixed and variable costs and CO2 emissions. A higher-level technology may reduce carbon emissions, but is likely to require more investment cost. The model considers CO2 emissions caused by all industrial and logistics operations as well as transportation. The main issues to be addressed in the sustainable SCND model includes determining the number, location, and technology level at plants and DCs, suitable transportation mode, and product flows between facilities. 2. Solution method We solve the corresponding bi-objective mixed integer linear programming model with the multi-directional local search (MDLS) framework. The efficiency of this recent framework has been proved on the multiobjective knapsack, set packing and orienteering problems, but to the best of our knowledge, this is the first attempt to solve a facility location problem with it. The MDLS is based on the principle of separately using independent single-objective local searches to iteratively improve the Pareto set approximation. The motivation for using this framework is the capability of using already implemented single objective optimization components. In our case, we use a large neighborhood search algorithm as single objective method. Our algorithm can be decomposed in the three following steps: Phase 1: look for an initial Pareto set approximation. The initial phase of the single objective LNS is executed separately for each objective. The output is an initial Pareto set approximation. Phase 2: Intensification around the Pareto set approximation. The Pareto set approximation is improved by exploring the neighborhood of all the solutions in this set with a Multi-directional local search. Phase 3: optimization of product flows. After stabilizing the location and transportation mode decisions for all Pareto set approximation solutions in phase 2, we determine the optimal product flows by applying the Simplex algorithm to all solutions in the set. 3. Computational results We assess the performance of our approach through a comparison with the well-known "-constraint method. In particular, we analyze the Pareto fronts given by both solutions on a set of 60 generated instances and show that the efficiency of our approach improves when the instance size grows.