In this comparative study, response surface methodology (RSM) was utilized to predict the surface roughness (Ra) and cutting force (Fz) when dry turning of AISI 316L austenitic stainless steel using cermet (GC1525) and coated carbide (GC1125) inserts. A constitutive relationship was attained correlating the prediction responses with three input parameters including cutting speed (Vc), feed (f), and depth of cut (ap). The models were developed using twenty-seven experiments carried out based on Taguchi L-27 orthogonal array. The formulated models' accuracy was checked based on the coefficient of determination (R-2), mean absolute percentage error (MAPE), and root mean square error (RMSE). Furthermore, three optimization methods, namely simulated annealing (SA), genetic algorithm (GA), and desirability function, were used to determine a set of optimal cutting parameters leading to minimize Ra and Fz separately and simultaneously. The results revealed that RSM models provided precise assessments of Ra and Fz. Regarding to the inserts' performance, it was obtained that the coated carbide insert produced better surface quality and minimum cutting force than the cermet insert. On the other hand, the cermet insert was found to have higher tool life than that of coated carbide insert with a ratio (tool life(GC1525)/tool life(GC1125)) of 1.25. Finally, according to optimization analysis, it was referred that the GA method was indicated better capability to achieve the optimum solutions that lead to the minimum Ra and Fz values separately and faster than the SA method. Proceeding from that, it was utilized in the multi-objective optimization in order to minimize Ra and Fz simultaneously and then compared with desirability function.