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The main objective of this work is to comprehensively investigate R450A behavior in refrigeration systems and subsequently optimize the main operating variables for the first time to reach the maximum performance. For this purpose, a hybrid multi-objective optimization model coupling response surface method and non-dominated sorted genetic algorithm II is established. The regression analysis results reveal a good agreement of experimental data samples with the quadratic polynomial models with a coefficient of determination exceeding 0.97. The optimum results for the first scenario indicate that the reduction in the motor-compressor electrical power consumption and discharge temperature is 19.72% and 54.21%, respectively, and percentage of growth in the refrigerant mass flow rate is 230.52% when the middle evaporator temperature, middle condenser temperature, superheating degree, and subcooling degree change from -14.95 o C to 8.71 o C, 31.28 o C to 24.50 o C, 13.12 K to 10.49 K, and 15.65 K to 15.66 K, respectively.