Robust design optimisation under mixed aleatory and epistemic uncertainty requires nested uncertainty propagation, Monte Carlo sampling for aleatory variables and min-max optimisation over epistemic intervals, leading to thousands of expensive model evaluations. For many expensive simulations, this becomes highly computationally intractable. This work demonstrates a surrogate-based framework which makes this optimisation more tractable.