This paper presents a technique to adapt HMMs to new speakers by using Genetic Algorithms (GAs) in unsupervised mode. The implementation requirements of GAs, such as genetic operators and objective function, have been chosen in order to give more reliability to a global linear transformation matrix. By implementing a ‘survival of the fittest’ strategy, the proposed GA-MLLR approach allows to maintain and manipulate a population of a wide range of solutions. Experiments have been performed on ARPA-RM and TIMIT databases using a triphones HMM-based system. Results show that from a new speaker, significant decrease of word error rate which can reach 6% for a particular speaker, has been achieved by the evolutionary approach, compared to the conventional MLLR-based adaptation method.