Uniformly grid sampling across the 5-dimensional input parameter space would be extremely labour and resource-intensive. Therefore, we employ Bayesian Optimization (BO) , which is a sample-efficient optimizer using the predictive capabilities of a surrogate model to suggest the next experiment(s) to perform, by which the model is updated on the new observations. This process is iterated until the desired optimization objective is attained. In a bi-objective optimization problem, both objectives must be balanced, where the optimal set of nondominated solutions (meaning there are no other solutions which are as good or better in both objectives) is denoted as the Pareto Front (PF). Hypervolume (HV) is an important performance metric which considers the space covered by optimal solutions, bounded by a reference point. To perform BO in this context, a multi-objective BO algorithm must be used.