Values of species richness are used widely to establish conservation and management priorities. Because inventory data, money, and time are limited, use of surrogates such as “indicator” species to estimate species richness has become common. Identifying sets of indicator species that might reliably predict species richness, especially across taxonomic groups, remains a considerable challenge. We used genetic algorithms and a Bayesian approach to explain individual and combined species richness of two taxonomic groups as a function of occurrence patterns of indicator species drawn from either both groups or one group. Genetic algorithms iteratively screen large numbers of potential models and predictor variables in a process that emulates natural selection. The best-fitting models of bird species richness and butterfly species richness explained approximately 80% of deviances and included only indicator species from the same taxonomic group. Using species from both taxonomic groups as potential predictors did not improve model fit but slightly improved the parsimony (fewer predictors) of the model of bird species richness. The best model of combined species richness included five butterflies and one bird and explained 83% of deviance, whereas a model of combined species richness based on six butterflies as indicators explained 82% of deviance. A model of combined species richness based on birds alone explained 72% of deviance. We found that a small, common set of species could be used to predict separately the species richness of multiple taxonomic groups. We built models explaining approximately 70% of the deviance in species richness of birds and butterflies based on a common set of three bird species and three butterfly species. We also identified a set of six species of butterflies that predicted ≥66% of both bird species richness and butterfly species richness. Our approach is applicable to any assemblage or ecosystem, and may be useful both for estimating species richness and for gaining insight into mechanisms that influence diversity patterns.