Applying twisted tape inserts as a passive improvement technique increases both pressure drop and heat transfer coefficient. In the design of heat exchangers, decreasing of pressure drop and increasing of heat transfer coefficient simultaneously comprise an important aim. In this study, multi-objective optimization is used to find optimum combinations of heat transfer coefficient and pressure drop during condensation of R404A vapor inside twisted-tape-inserted tubes. At first, Pareto-based multi-objective optimization is used to find the proper artificial neural networks based on the experimental data for prediction of heat transfer coefficient and pressure drop. In the next step, Pareto-based multi-objective optimization and previously obtained artificial neural networks are used to find optimal operation conditions that lead to optimum combinations of heat transfer coefficient and pressure drop. The corresponding optimal set of design variables, namely, mass velocity, vapor quality, and dimensional parameters of tubes, show the important design aspects.