This paper describes an investigation of the efficacy of various elitist selection strategies in a multiobjective Genetic Algorithm implementation, with parents being selected both from the current population and from the archive record of nondominated solutions encountered during search. It is concluded that, because the multiobjective optimization process naturally maintains diversity in the population, it is possible to improve the performance of the algorithm through the use of strong elitism and high selection pressures without suffering the disadvantages of genetic convergence which such strategies would bring in single objective optimization.