In this paper, we present an evolutionary approach to discover candidate haploty pes in a linkage disequilibrium study. This work takes place into the study of f actors involved in multi-factorial diseases such as diabetes and obesity. A firs t study on the linkage disequilibrium problem structure led us to use a genetic algorithm to solve it. Due to the particular, but classical, evaluation function given by the biologists, we design our genetic algorithm with several populatio ns. This model lead us to implement different cooperative operators such as muta tion and crossover. Probabilities of application of those mechanisms are set ada ptively. In order to introduce some diversity, we also implement a random immigr ant strategy and to cover up the cost of the evaluation computation we paralleli ze it in a master / slave model. Different combinations of the presented mechani sms are tested on real data and compared in term of robustness and computation c ost. We show that the most complete strategy is able to find the best solutions and is the most robust.