In this research we are trying to find the optimum point (or points) of effective parameters on heat transfer coefficient in nanofluids in which the heat transfer coefficient is the most inside the domain.Different parameters such as temperature, concentration of nanoparticles, flow pattern and etc. have different effects on thermal and heat transfer properties of a nanofluid and have been studied experimentally before by S. Zeinali Heris et al. In order to optimize, Genetic Algorithm Optimization Method has been applied. Since the available data is experimental and there is no equation to relate different parameters, the model created in previous study using artificial neural network (NN), is used in order to do the function evaluation.Experimental data of three different nanofluids, including nanoparticles Al2O3, CuO and Cu in water as the base fluid, flowing in circular tubes are available

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