The turning process is one of the significant machining processes widely applied in manufacturing industries. The study compared the minimum quantity lubrication turning process using sunflower oil lubrication and the dry turning process for AISI 316L material. In this study, a genetic algorithm was used to optimize material removal rate and surface roughness. Tool nose radius, cutting speeds, feed rates, and depth of cut was chosen as process parameters. The result of the process was a fitness function, which reflects the correlation between process parameters and material removal rate or surface roughness. The genetic algorithm uses the fitness function to yield optimum process parameters with the highest material removal rate and lowest surface roughness in a separate optimization process. The optimization method developed in the study can be applied to predict optimum material removal rate and surface roughness values for minimum quantity lubrication or dry turning process. The study concluded that the minimum quantity lubrication technique could yield favorable machining results with a higher material removal rate and lower surface roughness.