This article presents the state of the art of the application of optimization tools such as Genetic Algorithms, Simulation, Neural Networks, Markov Chains and Bayesian Networks in the physical asset maintenance management. The bibliographic references used were extracted from a detailed search that allowed the selection of the empirical studies presented, in the time horizon from 2010 to 2021, through databases, research platforms and online libraries. The analysis of the identified case studies is carried out, taking into account the variables involved in the study, the optimization tool used, and the result obtained in the analysis of the physical asset maintenance management. The benefits of the application of optimization tools are identified and it is confirmed that maintenance costs and intervention times are present variables, which contribute to the improvement of reliability and maintenance management.