In the last decades, a number of developments have made global optimization of large multi-dimensional design option spaces possible. Such developments include the increase in computing power, emergence of sophisticated optimization algorithms, and new techniques for the derivation of computationally highly efficient meta-models. Along with their promise, such developments also involve a number of potential drawbacks. For one thing, meta-models occasionally fail to capture the behaviour of "non-conventional" and complex designs. Another critical problem pertains to the potentially opaque nature of large-scale global optimization exercises, which make them less amenable to provision of intuitively graspable support in a naturally iterative design process. In this context, this research explores the potential of a novel approach toward iterative global optimization of locally optimized attribute clusters of building design solutions.