The application of multi-objective evolutionary computation techniques to the genetic programming of classifiers has the potential to both improve the accuracy and decrease the training time of the classifiers. The performance of two such algorithms is investigated on the even 6-parity problem and the Wisconsin breast cancer, Iris and Wine data sets from the UCI repository. The first method explores the addition of an explicit size objective as a parsimony enforcement technique. The second represents a program's classification accuracy on each class as a separate objective. Both techniques give a lower error rate with less computational cost than was achieved using a standard GP with the same parameters.