Considering the practical needs of equipment utilization, equipment preparedness, and the efficiency of equipment maintenance, the relationship between equipment utilization and maintenance was analyzed. A multi-objective optimization model aimed at maximizing equipment presence rate, equipment motor-hour reserve compliance rate, equipment availability rate, and minimizing equipment maintenance costs was established, with the constraint of balancing the revenue and expenditure of motor hours. Given the high-dimensional and large-scale difference characteristics of the established models objectives, an improved NSGA-Ⅲ algorithm was designed, which proposed the crossover and mutation evolutionary operations based on DNA, to enhance the evolutionary efficiency of the population. An adaptive normalization method was improved to enhance the population diversity and algorithm convergence speed. Taking the annual equipment utilization optimization as an example, comparative experiments were conducted, which verified the feasibility and efficiency of the proposed model and algorithm. By analyzing the trend of objective function values for a large number of equipment utilization plans and the detailed indicators of plans that achieve extreme values for single objectives, different preference-based equipment utilization optimization plans were proposed.