This paper examines the feasibility of using a quantum-inspired genetic algorithm (QGA) to solve the multi-objective job-shop scheduling problem (MOJSSP) on classical computers. Since most real-world problems addressed by evolutionary algorithms have extremely large search spaces, exploring and integrating quantum computing concepts into evolutionary algorithms to solve these problems presents an intriguing avenue for research. While quantum computing has the potential to solve combinatorial optimization problems, most existing quantum solutions require quantum hardware that is still limited experimentally. To address this, we investigate how quantum-inspired mechanisms, simulated on classical computers, can enhance traditional evolutionary algorithms for combinatorial optimization problems. Experimental results show that incorporating quantum-inspired components into a classical genetic algorithm can enhance solution quality and convergence in tackling the MOJSSP.