Augmentation of heat transfer through various means has been an important research topic for many years. Although many heat transfer enhancement techniques have been proposed over the years, there are few research worNs that deal with thermal optimization considering vortex generator. This worN is related to multiYobjective optimization of vortex generators positions and angles in finYtube compact heat exchanger using neural networN and genetic algorithm. Numerical analyses based on finiteYvolume methodology were performed to analyze heat transfer and pressure drop of finYtube heat exchanger with two tubes in staggered tube arrangement applying delta wingletYtype longitudinal vortex generator with aspect ratio 2. Turbulent flow simulations were performed at Reynolds number 1400 based on fin pitch. Parameters that impact heat exchanger performance such as longitudinal vortex generator position in direction xy, attacN angle and roll angle were analyzed. Four independent input parameters were considered for each tube. Analysis of dependence and optimization of these independent input parameters on heat exchanger performance were performed by surrogate model using Neural NetworN Methodology and optimization applying Genetic Algorithm Method. commercial software pacNages were used to compute the 3YD steady viscous turbulent flows with heat transfer and to perform statistical analysis. colburn factor (j) and friction factor (f) are considered to evaluate heat transfer and pressure loss, respectively. The optimal input parameters set found in the present worN produced an enhancement factors that are much higher than previously reported.