<p>Polylactide polymers (PLA) are widely used for coatings, semi-finished products, or finished components in various industrial applications. However, PLA polymers developed by 3D printing require functional performance related to the properties of their finished surfaces. Therefore, it is essential to master the 3D printing process for better performance and quantities of surface roughness. Thus, the choice of criteria for optimization and qualification of finished surfaces is crucial to guarantee the expected functional performance for a given industrial application. In the present work, we assume that the average amounts of arithmetic roughness (Ra) and profile irregularity (Rz) are not sufficient to correctly qualify a finite surface in terms of wear resistance or microstress concentration. Hence, the originality of this work lies in incorporating asymmetry (Rsk) and surface flattening (Rku) into the optimization of 3D printing conditions for PLA polymers. The response surface methodology (RSM) is adopted using a four-factor Box-Behnken (BBD) design. The results of the step-by-step iterative ANOVA regression analysis allowed us to fit the predictive models whose selectivity criteria are arithmetic roughness, profile irregularity, asymmetry and surface flattening. Considering its four selectivity criteria, the surface quality of PLA polymer samples has improved by more than 67%, with a multi-objective optimization achieving a desirability of approximately 96.34%. The study proved that multi-objective optimization is needed to identify the best combinations of process conditions. Thus, the optimal 3D printing conditions have been established for a printing temperature of 218 °C, a printing speed of 65.12 mm/s, a layer thickness of 0.12 mm and a ventilation rate of 80%. The combination of roughness (Ra), irregularity (Rz), asymmetry (Rsk), and surface flattening (Rku) has proven effective in enhancing wear resistance and reducing micro-stress concentrations on finished surfaces.</p>