A Nearest Neighbor Classifier (NNC) approaches the problem of text classification by computing a similarity metric between feature vector representations of an unknown document and a set of known prototype documents. The accuracy and speed of the NNC are dependent upon the choices of features and prototypes. In this work we consider the use of a genetic algorithm to optimize the feature and prototype sets for an NNC. We also examine whether simultaneously evolving the feature and prototype sets produces better results than sequential optimization.