The k-Nearest Neighbor classifier is a non-complex and widely applied data classification algorithm which does well in real-world applications. The overall classification accuracy of the k-Nearest Neighbor algorithm largely depends on the choice of the number of nearest neighbors(k). The use of a constant k value does not always yield the best solutions especially for real-world datasets with an irregular class and density distribution of data points as it totally ignores the class and density distribution of a test point’s k-environment or neighborhood. A resolution to this problem is to dynamically choose k for each test instance to be classified. However, given a large dataset, it becomes very tasking to maximize the k-Nearest Neighbor performance by tuning k. This work proposes the use of Simulated Annealing, a metaheuristic search algorithm, to select optimal k, thus eliminating the prospect of an exhaustive search for optimal k. The results obtained in four different classification tasks demonstrate a significant improvement in the computational efficiency against the k-Nearest Neighbor methods that perform exhaustive search for k, as accurate nearest neighbors are returned faster for k-Nearest Neighbor classification, thus reducing the computation time.