To the test the potency of our open-source implementations of NSGA-II and NSGA-III for multi-objective optimisation in drug design, we extend the use of tasks devised in the GuacaMol benchmarking suite by the bioinformatics company BenevolentAI. From the suite we selected multi-parameter optimisation (MPO) tasks with three or more objectives that aim to fine-tune the structural or physicochemical properties of five FDA-approved drugs: Cobimetinib (a mitogen-activated kinase inhibitor), Fexofenadine (a second-generation antihistamine), Osimertinib (a Tyrosine kinase inhibitor), Perindopril (a long acting ACE inhibitor), and Ranolazine (an anti-anginal drug). We search for a set of molecules that span the entirety of the Pareto front instead of trying to optimise a single value like the geometric mean.