In order to test Mexican areas of endemism of mammals identified by previous parsimony analyses of endemicity (PAEs), we applied the optimality criterion to three data matrices (based on point records, potential distributional models and the fill option in software NDM). We modelled the ecological niches of 429 terrestrial mammal species using the genetic algorithm for rule-set prediction (GARP) and models were projected as potential distributional areas. We overlapped the point occurrence data and the individual maps of potential distributions to a grid of 1° latitude–longitude. Three matrices of 247 grid cells (areas) and 429 species were built: (1) a binary matrix with ‘0’ for absence and ‘1’ for presence of at least one record of the species inside the grid-cell; (2) a three-state matrix similar to (1) but assigning the state ‘2’ to the assumed presence in the model of potential distribution; and (3) a three-state matrix similar to (2), but applying the fill option of software NDM instead of using a model. The optimality criterion was performed in NDM version 2.7 and results were examined with VNDM version 2.7. The first and second matrices showed 13 areas of endemism and the third identified 16 areas of endemism. NDM provided a better resolution than PAE, allowing us to identify several new areas of endemism, previously undetected. Ecological niche models, projected as potential distributional areas, and the optimality criterion are very useful to identify areas of endemism, although they should be used with caution because they may overpredict potential distributional areas. PAE seems to underestimate the areas of endemism identified. © 2009 The Linnean Society of London, Biological Journal of the Linnean Society, 2009, 98, 468–478.