The demand for individualized, small batch, and customized products in society can be satisfied by the flexible job shop system, which features numerous equipment, complex process paths, and varying failure frequencies. However, the single machine preventive maintenance approach currently employed to avoid equipment breakdowns increases the number of maintenance activities, maintenance costs, and negatively impacts production operations. To address the problems caused by traditional single machine preventive maintenance, this study proposes the application of a group preventive maintenance approach in the flexible job shop system. A joint mathematical model of group preventive maintenance and multi-objective flexible job shop scheduling is established. To overcome the local search limitations of traditional algorithms, a new multi-objective evolutionary algorithm is designed to solve the multi-objective flexible job shop scheduling problem, and demonstrate the application of the group preventive maintenance strategy in the flexible job shop system. Experimental results show that the designed multi-objective evolutionary algorithm can obtain more optimal solutions, has a faster convergence speed, and achieves better optimal solutions. Compared with the single preventive maintenance method, the group preventive maintenance approach results in fewer maintenance activities, lower maintenance costs, and less impact on production activities. The example results show that the group preventive maintenance time and maintenance cost are reduced by 150% compared with the single preventive maintenance method. The study proposes that the group preventive maintenance approach can be effectively used for the maintenance of production equipment in the semiconductor foundries in the future.