This study aims to design and implement a precise and scientific model for selecting green suppliers by integrating multi-objective mathematical programming and rough set theory in the Iran Khodro company. The research is applied–developmental in purpose and descriptive–analytical in method. Data were collected from field and library studies, Iran Khodro’s records, and three researcher-made questionnaires. The statistical population included managers and experts in procurement and environmental affairs and 18 qualified suppliers. Based on the Morgan table, 66 respondents participated. Content validity was confirmed (CVI=0.91) and reliability verified (Cronbach’s α>0.83). Data analysis employed t-tests, confirmatory factor analysis, a hybrid multi-objective programming model, and rough set theory. Environmental indicator weights were determined by rough sets, and the multi-objective model was transformed into a single-objective linear model solved using WinQSB. Four main criteria—cost, quality, service level, and environmental performance—were found critical in supplier selection. The calculated weights were 0.54, 0.12, 0.19, and 0.15 respectively. Out of 18 suppliers, 12 were identified as optimal under capacity and demand constraints. The optimal model yielded a minimum cost of 15.885 billion IRR, maximum service improvement of 2 days, and a defective rate of 2.65%. The top suppliers achieved an average environmental score exceeding 4 points, reflecting alignment with sustainability principles. Integrating multi-objective programming with rough set theory provides an effective, flexible approach for green supplier selection. The model incorporates both quantitative and qualitative indicators under realistic constraints, enhancing multi-criteria decision-making and improving environmental performance in supply chains. Application of this model in highly polluting industries such as automotive, chemical, and petrochemical sectors can optimize resource allocation, reduce emissions, and strengthen sustainability.