The main difficulty in multi-objective optimisation is balancing different target properties to maximise the use of available data. For physical experiments, the cost of measuring different properties can vary substantially. For example, optical properties can often be measured efficiently through in situ spectroscopic techniques, whereas surface morphology requires more laborious experimental procedures. It is desirable to first explore the objectives with lower to locate regions with suitable values, before continuing the search across more expensive objectives within these regions. This procedure can reduce the overall cost and the initial data can be applied to design a new target space with less complexity by dimension reduction. Further cost reductions can be achieved by combining multi-objective Bayesian optimisation with parallel search methods.