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When computational methods are developed, the efficiency of the novel methods should be compared to the existing ones. This can be done using, e.g., analytical methods and benchmark test patterns. In addition, the comparison of the best and the worst case performance is commonly of interest. In this paper, methodologies of genetic algorithm based software testing are adopted to the comparative computational testing of three varieties of dynamic two-dimensional nearest point algorithms. The extreme performances of the algorithms are searched for by optimizing the shape of two-dimensional Gaussian distributions, from which the test patterns are drawn. In particular, an approach to pairwise comparisons of computational complexities of algorithms is proposed. The test case algorithms can be sorted with respect to their computational complexity by the proposed method.