Support vector machines is a very popular method in classification of hyperspectral images due to their good generalization capability even with a limited number of training datasets. However, the performance of SVM strongly depends on selection of kernel parameters when RBF kernel is used. In order to achieve a high classification performance, the kernel parameters, that are the value of regularization term and kernel width, should optimally be chosen. In this work, the use of recently developed evolutionary optimization methods, harmony search and differential evolution methods, are investigated in the context of hyperspectral image classification for the first time in this paper. The experimental results showed that these methods provide fast and accurate results in comparison to classical grid search approach.