One of the major issues confronting management of parks and reserves is the invasion of non-native plant species. Yosemite National Park is one of the largest and best-known parks in the United States, harbouring significant cultural and ecological resources. Effective management of non-natives would be greatly assisted by information on their potential distribution that can be generated by predictive modelling techniques. Our goal was to identify key environmental factors that were correlated with the percent cover of non-native species and then develop a predictive model using the Genetic Algorithm for Rule-set Production technique. We performed a series of analyses using community-level data on species composition in 236 plots located throughout the park. A total of 41 non-native species were recorded which occurred in 23.7% of the plots. Plots with non-natives occurred most frequently at low- to mid-elevations, in flat areas with other herbaceous species. Based on the community-level results, we selected elevation, slope, and vegetation structure as inputs into the GARP model to predict the environmental niche of non-native species. Verification of results was performed using plot data reserved from the model, which calculated the correct prediction of non-native species occurrence as 76%. The majority of the western, lower-elevation portion of the park was predicted to have relatively low levels of non-native species occurrence, with highest concentrations predicted at the west and south entrances and in the Yosemite Valley. Distribution maps of predicted occurrences will be used by management to: efficiently target monitoring of non-native species, prioritize control efforts according to the likelihood of non-native occurrences, and inform decisions relating to the management of non-native species in postfire environments. Our approach provides a valuable tool for assisting decision makers to better manage non-native species, which can be readily adapted to target non-native species in other locations.