Computing consensus trees amounts to finding a single tree that summarizes a collection of trees. Three evolutionary algorithms are defined for this problem, featuring characteristics of genetic programming (GP), evolution strategies (ES) and evolutionary programming (EP) respectively. These algorithms are evaluated on a benchmark composed of phylogenetic trees computed from genomic data. The GP-like algorithm is shown to provide better results than the other evolutionary algorithms, and than two greedy heuristics defined ad hoc for this problem.