Design of optimal microstructures for infiltrated solid oxide fuel cell (SOFC) electrodes is a complicated task because of the multitude of electro-chemo-physical phenomena taking place simultaneously that directly affect working conditions (cell temperature, current density, and flow rates) of the SOFC electrode and therefore its performance. In this study, an innovative design paradigm is presented to obtain a part of geometry-related electrochemical and physical properties of an infiltrated SOFC electrode. A range of digitally realized microstructures with different backbone porosity and electrocatalyst particle loading under various deposition conditions are generated. Triple phase boundary (TPB), active surface density of particles, and gas transport factor are evaluated in realized models on the basis of selected infiltration strategy. On the basis of this database, a neural network is trained to relate desired range of input geometric parameters to a property hull. The effect of backbone porosity, loading, distribution, and aggregation behavior of particles is systematically investigated on the performance indicators. It is shown that from the microstructures with very high amount of TPB and particle contact surface density, a relatively low gas diffusion factor should be expected; meanwhile, increasing those parameters does not have sensible contradiction with each other. Excessive agglomerating of particles has a negative effect on TPB density, but the distribution of seeds always has a positive effect. Direct search and genetic algorithm optimization techniques are used finally to achieve optimal microstructures on the basis of assumed target functions for effective geometric properties.