Nowadays, fossil energy sources are decreasing gradually and environmental problems are increasing thus energy savings are among the current topics. In this context, determining the optimum design parameters of heat exchangers can provide significant savings during the economic life of waste heat recovery systems. However, due to discrete decision variables, highly nonlinearity, trapped to local optimum and high computation time, the optimization of a heat exchanger can be a robust problem. Therefore, this research introduces Univariate Search Method 2 (USM2) that can cope with such difficulties for multi-objective optimization. USM2 is based on weighted sum approach and uses a single-objective optimization method by moving from two arms on Pareto Front. To short computational time, Univariate Search Method (USM) has been chosen as a single-objective optimization method. The well-known Genetic Algorithm (GA) and Non-dominant Sorting Genetic Algorithm-2 (NSGA-2) methods were also used to compare USM and USM2 with fitness values and number of the function call corresponding to the computational time. According to the results, GA has offered better fitness values for both objectives than USM yet GA has much more number of the function call. However, USM2 has offered a better design range in the Pareto-Front than NSGA-2 despite having almost half of the function call number of NSGA-2. Moreover, the solutions offered by the USM2 at the ends of the Pareto-Front are highly competitive when compared to GA's solutions.