The design of a resilient and sustainable supply chain network is a prolific field to be studied academically, which can potentially develop and affect supply chain performance. The innovation of this research is a closed-loop supply chain network by taking the sustainability, resilience, robustness, and risk aversion approach into consideration. A two-stage, mixed-integer linear programming is used for modeling and a robust counterpart model is utilized to encounter the demand uncertainties. The Conditional Value-at-Risk criterion is considered to model risk and compared with Value-at-Risk and average absolute deviation. Sustainability goals addressed in this research include minimizing the costs, CO2 emission, and energy, and maximizing the employment. The case study in this research is an automobile assembly company that has decided to set up a supply chain network. The LP-Metric method is applied to merge objectives and NEOS server is employed to attain an optimal solution in large scale. The constraint relaxation and fix-and-optimize are employed to produce the upper and lower bounds in medium and large scale. The results showed that the proposed model provides a better estimation of the total cost, pollution, energy consumption, and employment level compared to the basic model.