The topic of optimizing neural networks parameters has garnered considerable attention over the years, more so with the continued succession of ever larger, denser networks. In this work, we present evaluation and analysis of our proposed Weight Pruning Genetic Algorithm, with pruning specific mutators. The results show that evolved weight pruning of MNIST trained multilayer perceptrons and convolutional networks can remove as much as 72.4% and 89.6% of fully connected layer parameters, and yield improvements in test set accuracy without the use of retraining.