Effective decision-making in supply chain management is paramount for attaining operational excellence, adjusting to market trends, and satisfying consumer expectations. Decision-making aids in identifying, assessing, and mitigating risks, assuring the supply chain’s resilience to unanticipated problems. It requires a comprehensive and strategic strategy to address the different linked parts of the supply chain ecosystem. Being so pertinent in nature, uncertainty in demand, supply chain interruptions, information shortfalls, and time restrictions all complicate supply chain management decisions. To navigate this complexity, the paper approaches a proactive and flexible approach to solve multi-objective decision-making problems leveraging a heuristic genetic algorithm-non-dominating sorting algorithm (NSGA-3). The paper aims to use a genetic approach (NSGA-3) to prioritize the internal and external driving forces and simplify the decision-making process in the supply chain industry. NSGA-3 helps solve and decide on multi-objective problem statements while optimizing the supply chain network with minimal human intervention. Here for example, profit is maximized while minimizing the compliance threshold, but other multi-objective problem statements can also be considered. The outcomes of NSGA 2 and NSGA 3 are contrasted, the key performance metrics used, and the benefits of NSGA-3 over NSGA-2 are also covered.