A potential area of difficulty for Multi-Objective Optimisation of industrial problems is a class of problems where the majority of the objective space violates blackbox constraints. The difficult arises because potential solutions that violate blackbox constraints provide no information beyond their infeasibility. They provide neither meaningful information about their objective values nor about the degree to which the constraint is violated (or even in some cases which constraint is violated). This means that they do not help to find valid solutions (except by elimination) which, in turn, reduces the early stages of optimisation to effective guesswork until some feasible solutions are found. In this work, we attempt to reduce this problem by using a Decision Tree to identify and repair infeasible solutions by learning the underlying constraints on each parameter. We propose three potential Pre-Repair Methods and compare them on a modified case study of an airfoil lift/drag optimisation problem. Note that no optimisation was done; instead the goal was to decide if the repair methodologies were suitable in the problem space. We used two baselines: not using a Decision Tree, and only using a Decision Tree to identify potentially infeasible solutions for complete regeneration. All three of our proposed methods outperformed the baselines at a statistically significant level of confidence of 0.001.