The PC/ABS polymer blend is widely recognized as one of the most commercially successful polymer blends, primarily used in the automotive and consumer electronics industries. Its success stems from the synergy between the excellent thermal and mechanical properties of PC and the exceptional toughness of ABS, itself a binary blend of SAN and rubber particles. Depending on the loading conditions and the morphology of the blend, different deformation mechanisms govern the response of the material, namely the internal particle cavitation of the rubber particles and the debonding at the interface between PC and ABS. Within this setting, modeling its behavior and optimizing the design of its microstructure is a challenging task. To address these challenges, in this study, a multi-scale framework is proposed to model the response of different PC/ABS blends and to optimize their design. This framework relies on (i) the efficient generation of suitable Representative Volume Elements for the PC/ABS microstructure [1], on (ii) the establishment of accurate constitutive models for the blend phases [2], and on (iii) the development of an efficient and unsupervised framework for optimizing the design of the blend’s microstructure [3]. Within this setting, the content of ABS in the blend, the content of rubber in the ABS and the orientation angle of the ABS particles are selected such as the blend presents maximum toughness, strength as well as minimum cost and weight. Due to the high-dimensionality of the output space, some Multi-Criteria Decision Making techniques are explored to select the best solution within the Pareto front. Overall, the numerical results validate the proposed design framework for PC/ABS, but also demonstrate a high flexibility of application to different materials and structures. [1] J. L. Vila-Chã, B. P. Ferreira, F. M. Andrade Pires. Mechanics of Materials 163 (2021) 104069. [2] A. F. Carvalho Alves, B. P. Ferreira, F. Andrade Pires. International Journal of Solids and Structures 283 (2023). [3] R. C. Coelho, A. F. Carvalho Alves, T. M. Nogueira Pires, F. M. Andrade Pires. Computer Methods in Applied Mechanics and Engineering 434 (2024) 117516.