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The study of vegetation response to environmental change on a global scale cannot rely on species because most plant species have geographically limited distributions. To allow ecological predictions beyond the scale of the floristic region, models have to rely on vegetation descriptions using plant types other than the species. The crucial problem is how to define the types. Since types are described by traits, the problem translates into one of optimal trait selection. The best plant traits are those that when used to define plant types optimize the perception of association between vegetation and environmental (e.g., climate, disturbance) variation. I consider trait selection as a two-step procedure. The first step is the selection of a larger trait set based on past experience and known practicality, which is used for community description. The second step, for which the paper describes new methods, is accomplished on the data analytically by suitable computer algorithms that can find the optimal subset among the pre selected traits. This subset defines optimal plant functional types (PFTs). The methods involve a fuzzy set approach and community description by plant types. The optimization algorithms described are tested with data from plant communities in South and North America. The utility of the approach in the evaluation of convergence of phylogenetically distant plant communities is discussed.