This work shows the multi-criteria data analysis of the dry and MQL turning process of AISI 316L steel using the evolutionary algorithms of non-dominant class II and III (NSAG-II and NSAG-III). The wear of the cutting tool (VB), the energy consumption (E) and the machining time (t) are used as analysis variables, with the aim of minimizing the wear of the cutting tool based on the optimal selection of parameters. When comparing the results obtained from both methods, we found that NSAG-III was the best alternative for selecting parameters in the turning of specimens, with fewer tool wear and more efficient use of energy consumption. Interpretation of this data