In line with growing global concerns regarding environmental and social issues, supply chaincorporations are improving their environmental and social performances. The optimal design of a closedloop supply network must conceive various aspects, leading to a multi-objective problem. This study developsa mixed-integer linear programming model to provide an integrated supply network with a particular focuson sustainability. Besides cost efficiency, energy consumption, and job creation are incorporated asadditional objective functions. This article uniquely introduces the training of supply chain employees as partof the developed model to address social responsibility. The Non-Dominated Sorting Genetic Algorithm-II(NSGA-II) and Multiple Objective Particle Swarm Optimization (MOPSO) are employed to solve the multiobjective problem. The numerical examples for cost and energy values are based on real data. The resultsdemonstrate the significant effect of returned product recovery on cost reduction in the network and changesin energy consumption at different levels. NSGA-II and MOPSO yield a set of optimal solutions that increasethe flexibility of decision-makers. Indeed, a set of Pareto solutions reveals a conflict between the objectivefunctions and allows the network to be highly effective in decision-making under different conditions andpolicies.