The residual stresses induced during shaping and machining play an important role in determining the integrity and durability of metal components. An important issue of producing safety critical components is to find the machining parameters that create compressive surface stresses or to minimize tensile surface stresses. In this article, a systematic data-driven fuzzy modeling methodology is proposed, which allows constructing transparent fuzzy models considering both accuracy and interpretability attributes of fuzzy systems. The new method employs a hierarchical optimization structure to improve the modeling efficiency, where two learning mechanisms cooperate together: the Nondominated Sorting Genetic Algorithm II (NSGA-II) is used to improve the model's structure, while the gradient descent method is used to optimize the numerical parameters. This hybrid approach is then successfully applied to the problem that concerns the prediction of machining induced residual stresses in aerospace aluminium alloys. Based on the developed reliable prediction models, NSGA-II is further applied to the multiobjective optimal design of aluminium alloys in a oreverse-engineeringo fashion. It is revealed that the optimal machining regimes to minimize the residual stress and the machining cost simultaneously can be successfully located.