This thesis has evaluated the use of the computationally expensivemaximum-likelihood (ML) method coupled with an evolutionaryalgorithm (EA) for the problem of inferring evolutionaryrelationships among species (phylogenies) from molecular data. MLmethods allow using all the information from molecular data, suchas DNA sequences, and have several beneficial properties compared toother methods. Evolutionary algorithms is a class of optimizationalgorithms that often perform well in complex fitness landscapes.EAs are also proclaimed to be easy to parallelize, an aspect thatis increasingly more important.A parallel EA system has been implemented and tested on a clusterfor the task of phylogeny inference. The system shows promisingresults and is able to utilize processors of a massively parallelsystem in a transparent manner.