Aerospace design is synonymous with the use of long running and computationally intensive simulations, which are employed in the search for optimal designs in the presence of multiple, competing objectives and constraints. The difficulty of this search is often exacerbated by numerical `noise' and inaccuracies in simulation data and the frailties of complex simulations, that is they often fail to return a result. Surrogate-based optimization methods can be employed to solve, mitigate, or circumvent problems associated with such searches. This presentation gives an overview of constrained multi-objective optimization using Gaussian process based surrogates, with an emphasis on dealing with real-world problems.