3D printing manufactures products layer by layer, promoting the conservation of natural resources while offering an economical and safe choice for workers, communities, and consumers. Certain features can be integrated into the 3D printing process by developing mathematical models that determine the optimal parameters for achieving optimal printing outcomes. This research creates a mathematical model for multi-objective optimization aimed at identifying the best 3D printing process parameters. The study’s findings on Multi-Objective Optimization Modeling in 3D Printing of PLA+ using Response Surface Methodology (RSM) and NSGA II have significant practical implications. By integrating RSM and NSGA II, the study enables a more precise and efficient optimization of key 3D printing parameters, such as print speed, temperature, and layer height. This leads to enhanced print quality, reduced material waste, and optimized mechanical properties of the printed parts. This study involved several decision variables, such as infill density, nozzle temperature, printing speed, layer thickness, and bed temperature. This study has two objective functions, such as maximizing tensile strength and minimizing surface roughness. The Gamultiobj algorithm (MATLAB) and the non-dominated sorting genetic algorithm II method have the specific advantage of simultaneously searching for optimal values for the four objective functions. The study found that the best level setting for the infill density was 99.992%, 214.998 ℃ for the nozzle temperature, 70.000 mm/s for the printing speed, 0.151 mm for the layer thickness, and 48.390 ℃. The optimal response is a tensile strength of 31.399 MPa and a surface roughness of 10.290 μm.