Abstarct: The rapid rise in world energy sources has led to a shortage of energy supplies and environmental pollution. Renewable energy sources can be a substitute for conventional energy sources, these sources include solar energy, geothermal or heat loss from industrial processes. The Organic Runnin Cycle (ORC) is recognized as an acceptable technology for converting thermal energy to electrical energy at low thermal sources, which is used in this cycle from organic fluids as working fluid. Since the heat exchangers within the organic Rankine cycle an important role in the cycle. As well as a significant proportion of the heat dissipation and the cost of the heat exchangers cycle so optimizing the heat exchanger in this cycle is importan. In this research, a plate heat exchanger as an evaporator in the organic Rankine cycle and R123 fluid has been selected as a working fluid. Water vapor with an inlet temperature of 150 ° C is considered as a hot fluid. Here, we aimed to optimize the multi-objective plate heat exchanger using genetic algorithm to obtain the optimal design of geometric parameters, which resulted in the lowest pressure drop and maximum overall heat transfer coefficient is determined. Obviously, the two objective functions are opposite to each other, therefore, in this optimization method, the Pareto solution is used, which can be lixnked simultaneously between objective functions and geometric parameters of the heat exchanger according to the constraints, provide. In this research, the sensitivity analysis of each of the geometric parameters (independent variables) has been investigated and the effect of each parameter on the t objective functions has been investigated, which one of the important results of this study is that by increasing the number of plates, the pressure drop and the overall heat transfer coefficient decrease. Also the effects of heat exchanger performance parameters on optimum design of heat exchanger is studied. Including the parameters studied in this study is the mass flow rate of hot and cold fluids which with increases mass flow rate, total pressure loss and overall heat transfer coefficient increases, which the effect of this increase is far greater in the pressure drop and finally, in optimization using the genetic algorithm, the pressure drop can be reduced by increasing the number of pages.