A Brain Computer Interface (BCI) provides a direct form of communication between a person and the outside world using brain signals, either to increase his/her integration in society or to provide a way to control the environment where he/she lives. BCIs are communication systems based on electroencephalographic (EEG) signals, such as event-related evoked potentials (ERP). P300 is one of there ERP. It is a peak that usually appears in the EEG signals around 300 ms in response to an infrequent stimulus. The BCI based on P300 is usually composed by different blocks: input (data acquisition), feature selection/extraction, classification, output (e.g. control commands) and, eventually, feedback. In this work, a Genetic Algorithm (GA) is proposed as a feature selection method before the classification stage, implemented using Fisher’s Linear Discriminant Analysis (LDA). A dataset of input patterns was generated from a database of EEG recordings of healthy people, in order to train and test the proposed configuration. The addition of the GA as a feature selection method resulted in a significant improvement in classification performance (p < 0.001) and in a reduction of the amount of features needed to reach such performance (p < 0.001). The results of this work suggest that this configuration could be implemented in a portable BCI.