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Ecological niche models and species distribution models are used in many fields of science. Despite their popularity, only recently have important aspects of the modeling process like model selection been developed. Choosing environmental variables with which to create these models is another critical part of the process, but methods currently in use are not consistent in their results and no comprehensive approach exists by which to perform this step. Here, we compared seven heuristic methods of variable selection against a novel approach that proposes to select best sets of variables by evaluating performance of models created with all combinations of variables and distinct parameter settings of the algorithm in concert. Our results were that-except for the jackknife method for one of the 12 species and fluctuation index for two of the 12 species-none of the heuristic methods for variable selection coincided with the exhaustive one. Performance decreased in models created using variables selected with heuristic methods and both underfitting and overfitting were detected when comparing their geographic projections with the ones of models created with variables selected with the exhaustive method. Using the exhaustive approach could be time consuming, so a two-step exercise may be necessary. However, using this method identifies adequate variable sets and parameter settings in concert that are associated with increased model performance.