Job Shop Scheduling Problem (JSSP) is a famous Combinatorial Optimization challenge in operations research, computer science and industrial engineering. Traditional optimization methods often struggle with computational complexity, making them unsuitable for mass and dynamic scheduling problems. This paper proposes the use of Social Group Optimization technique (SGO) a novel population based technique inspired by the human social behaviour to address these kind of challenges. The insights between SGO, PSO, ACO and GA is presented. The result demonstrate that SGO performs well in all benchmarks. Experimental results on JSSP benchmarks prove that they outperform traditional heuristics in computational efficiency and solution quality. These give insights into real-world applications in construction, logistics, and service operations.