The rise in global carbon dioxide levels necessitates efficient, low-pollution energy technologies. Solid Oxide Fuel Cells (SOFCs) are promising energy converters, and their electrical performance is strongly influenced by the electrode microstructure. This study presents a comprehensive multiscale, experimentally grounded optimization pipeline for SOFC electrodes to maximize the electrical power density, integrating microscale and macroscale approaches. The methodology combines tomography-based microstructure characterization, computational homogenization, multiphysics simulations, model order reduction, and machine-learning-based surrogate modeling. Anode samples with fine, medium, and coarse grain sizes are analyzed using high-dimensional morphological descriptors to characterize microstructure morphology. Partial least squares discriminant analysis reduces the descriptor space to enable efficient surrogate modeling and generation of artificial microstructures by interpolation in the reduced space. Effective conductivities and permeability are computed by first-order homogenization and incorporated into a macroscopic fuel cell model to predict the power density. The proposed framework links microstructural information to macroscopic electrical performance within a nested optimization loop, enabling systematic exploration of physically realistic microstructural variants. Using a Ni-YSZ anode as a case study, the approach identifies the most suitable microstructure characteristics within an experimentally limited design space and provides a flexible optimization framework that can be adapted to different databases, models, and objective functions.