Modern engineering design optimization relies to a large extent on computer simulations of physical phenomena. The computational cost of such high-fidelity physics-based analyses typically places a strict limit on the number of candidate designs that can be evaluated during the optimization process. The more global the scope of the search, the greater are the demands placed by this limited budget on the efficiency of the optimization algorithm. This thesis proposes a number of enhancements to two popular classes of global optimizers. First, we put forward a generic algorithm template that combines population-based stochastic global search techniques with local hillclimbers in a Lamarckian learning framework. We then test a specific implementation of this template on a simple aerodynamic design problem, where we also investigate the feasibility of using an adjoint flow-solver in this type of global optimization. In the second part of this work we look at optimizers based on low-cost global surrogate models of the objective function. We propose a heuristic that enables efficient parallelisation of such strategies (based on the expected improvement infill selection criterion). We then look at how the scope of surrogate-based optimizers can be controlled and how they can be set up for high efficiency.