This paper presents an application of artificial neural networks together with genetic algorithm on experimental tests to optimization and intensification the thermo-hydraulic behavior of a corrugated tube heat exchanger equipped with twisted tapes. The experiments on conventional, perforated, V-cut and U-cut twisted tape inserts are done on turbulent regimes with Reynolds numbers from 5000 to 15000 while the twist ratios are 3, 5 and 7. Finally, the results are optimized using multi-objective genetic algorithm to find out the optimal cases for thermal performance factor (R3). The average differences between the artificial neural network prediction and experimental results are found very low and even less than 2%. The results show that the corrugated tube with V-cut twisted tape has the maximum heat transfer rate while the minimum pressure drop belongs to the perforated twisted tape. Also the thermal performance factor is always more than one and the maxima is happened at V-cut twisted tape with R3 = 1.50.