Given the increasing demand for laboratory equipment management in universities, especially the increasingly complex equipment management, the traditional equipment management system can no longer meet the management needs of universities. Therefore, it is very important to optimize the university’s equipment management system. The allocation of laboratory equipment maintenance tasks in the laboratory equipment management system of universities is a very critical link. Its effective solution is crucial to ensure the normal operation of laboratory equipment and the reasonable allocation of maintenance resources. This study proposes a double coding adaptive genetic algorithm to optimize the allocation of laboratory equipment maintenance tasks in universities to achieve the optimal allocation of resources and minimize maintenance costs. The work allocation scheme is iteratively optimized by a dual-coding strategy and definition of adaptive crossover and mutation operators. The experimental results of this study show that the algorithm can find the approximate optimal task allocation scheme within a reasonable time, which improves the efficiency and accuracy of laboratory equipment maintenance. In addition, compared with the traditional allocation method, the algorithm in this paper shows stronger flexibility and robustness when dealing with large-scale complex problems.