Many questions in comparative biology require that new data be collected, either to build a comparative database for the first time or to augment existing data. Given resource limitations in collecting data, which species should be studied to increase the size of comparative datasets? By taking the hypotheses, other comparative data relevant to the hypotheses, and an estimate of phylogeny, we show that a method of “phylogenetic targeting” can systematically identify the species to study. Phylogenetic targeting selects potential candidates for future data collection based on a flexible scoring system that maximizes the differences in pairwise comparisons while taking potentially confounding variables into account. The method can control for confounding variables, or it can maximize the power to test competing hypotheses. We used simulations to assess the performance of phylogenetic targeting, as compared to a less systematic approach of randomly selecting species (as might occur when data have been collected on species without regard to variation in the traits of interest). The simulations revealed that selection of species using phylogenetic targeting increased the statistical power to detect correlations and that power increased with the number of species in the clade even when the number of samples collected was not increased. We also developed a web-based, freely available and publicly accessible computer program called PhyloTargeting to implement the approach. It provides a user-friendly interface, a variety of options to analyze the dataset, and graphical visualizations of the results.