Scheduling plays a vital role in various industries especially in auto industries, where job and machine should be arranged in an appropriate sequence for effective outcome. To pursue this type of sequence manually takes a long time and is complex to compute. This urges to incorporate optimization techniques to predict the optimal job and machine sequence. This research incorporates five different sizes of Bench Mark (BM) Job Shop Scheduling Problems (JSSP). These problems intent to solve with the aid of Teaching–Learning Based Optimization (TLBO), Greedy Randomized Adaptive Search Procedure (GRASP), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Artificial Fish Swarm Optimization (AFSO). The investigation reveals the superiority of proposed AFSO over other comparative techniques in all performance evaluations.