In the machining process, the proper selection of cutting parameters can significantly reduce power consumption. Over the past several years, numerous researchers have devoted considerable attention to optimizing cutting parameters for milling processes to save energy consumption. The correct selection of these parameters is a crucial approach to achieving an optimal machining process. This article presents a study on the optimization of cutting parameters in the milling process of AISI 1045 steel under wet conditions. The aim is to identify an ideal combination of cutting parameters that minimizes the material removal power ( \(P_{m} )\) . Taguchi and Genetic Algorithm (GA) with two different selection methods are proposed for optimizing four cutting parameters namely cutting depth ‘ \(a_{p}\) ’, width of cut ‘ \(a_{e}\) ’, cutting speed ‘ \(v_{c}\) ’, and feed per tooth ‘ \(f_{z}\) ’. The results demonstrated that GA with tournament selection successfully identifies the optimal cutting parameters, resulting in lower \( P_{m}\) , compared to GA with stochastic selection. The findings of this study provide valuable insights that could lead to more energy-efficient machining processes, contributing to both cost savings and environmental sustainability.