The Negative Selection (NS) algorithm is the first algorithm to come from the study of natural immune systems, and is the most widely known and applied algorithm in the field. It has been used to build intrusion detection systems along with many other security-related tasks. However, it has not been possible to use the Negative Selection algorithm on many real-world scenarios. The present research shows an optimization of the negative selection algorithm to make its execution faster. The optimized algorithm remains functionally the same, providing the same results as the unoptimized algorithm. Details are given about the optimization scheme used and the optimized negative selection algorithm is tested on the UCI Breast Cancer data set. The performance of the unoptimized negative selection algorithm is compared to the performance of the algorithm with the proposed optimization. Three claims about the function of the optimized negative selection algorithm are made and tested with four experiments. The results of the experiments are used to demonstrate that the algorithm is faster and does not change the negative selection algorithm or lower its accuracy. Although there has been research into the optimization of the Negative Selection algorithm, this work will only apply to hyper-sphere detectors, which has not been done before.