How do we find the optimal solution for a constrained multi-objective optimisation problem when we have no analytical expression for the objective functions, and very limited function evaluations within the huge search space due to the expense of measuring the objective functions? Calculus can’t help you, and trial and error is not an option! This talk will describe a common practical optimisation problem found in many industrial settings with these challenges, and introduce some methods for expensive black-box optimisation. Finally, we will address the question of how best to evaluate the performance of such methods by generating new test instances with controllable characteristics.