Multi-objective optimisation focuses on optimising multiple objectives simultanuously. Evolutionary and immune-based algorithms have been developed in order to solve multi-objective optimisation problems. These algorithms often include a property called elitism, a method of preserving good solutions. This study has focused on how different approaches of elitism affect an algorithm's ability to find optimal solutions in a multi-objective optimisation problem with a discrete and highly discontinuous decision space. Three state-of-the-art algorithms, NSGA-II, SPEA2+ and NNIA2, were implemented, validated and tested against a multi-objective optimisation problem of a miniature plant. Final populations yielded from all the algorithms were included in an analysis. The results of this study indicate that external populations are important in order for algorithms to find optimal solutions in multi-objective optimisation problems with a discrete and highly discontinuous decision spaces.