As different region of the brain is associated with different mental activity, channel selection is commonly used to enhance the performance of multi-electrode electroencephalography (EEG) system by removing task-irrelevant and redundant channels. Various channel selection methods are successfully implemented in Brain-Computer Interface (BCI) system that uses one type of brain activity by earlier researchers. Upon realizing the limitation of conventional BCI systems, there has been increasing number of hybrid BCI systems. These hybrid systems use combinations of two brain activity patterns to enhance the functionality of a system. In this paper, three multi-objective genetic algorithms (GAs) are proposed to optimize the number of channels selected and system accuracy. The objective of this research is to investigate the optimal tradeoff between the classification accuracy of a BCI system and the number of selected channels. This tradeoff is important because different BCI applications have different priorities; some implementations prefer minimum number of channels while others favor the classification accuracy. The second objective of this research is to investigate the effectiveness of the GAs adopted as a channel selection method for BCI systems based on different brain activity. Three BCI Competition data sets are used to evaluate the performance of the proposed GAs. Non-parametric Friedman test (p-value=0.635) is also conducted and the result reveals that the significant reduction in number of channels does not have significant impact in the classification accuracy on the evaluation data. This confirms the validity of genetic algorithms as a channel selection method for both P300 and motor imagery data.