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Accurate estimation of evapotranspiration (ET) values is of crucial importance in hydrology, agriculture and agro-meteorology issues. The objective of this research was to evaluate the use of evolutionary support vector machine (ESVM) to model daily ET using limited climatic data. For this aim, the most common evolutionary method, genetic algorithm (GA), was used for optimization of SVM variables. For the ESVM, four input combinations of maximum air temperature (T-max), minimum air temperature (T-min), wind speed (U-2), daily solar radiation (Rs), relative humidity (Rh-mean) and mean temperature (T-mean) were tried. Climatic data covering 3-year period of October 2004-October 2007 were obtained from the extremely arid and hot region of Haji Abad located in the northern region of Hormozgan province, Iran. Artificial Neural Network (ANN) as a base model was also applied for evaluating modeling accuracy of the ESVM in estimating ET. The results of the ESVM and ANN models were evaluated by comparing their estimates with the measured lysimetric data. The root mean square error (RMSE), coefficient of efficiency (CE) and the coefficient of determination (R-2) were used as comparison criteria. According to the results obtained, the ESVM2 whose input variables are T-mean and Rh-mean was selected as the best model in estimating ET.