Metallic foams or sponges are materials with a cell structure suitable for many industrial applications, such as reformers, heat catalytic converters, etc. The success of these materials is due to the combination of various characteristics such as mechanical strength, low density, high specific surface, good thermal exchange properties, low flow resistance and sound absorption. Different materials and manufacturing processes produce different type of structure and properties for various applications. In this work a genetic algorithm has been developed and applied to support the design of catalytic devices. In particular, two substrates were considered, namely the traditional honeycomb and an alternative open-cell foam type. CFD simulations of pressure losses and literature based correlations for the heat and mass transfer were used to support the genetic algorithm in finding the best compromise between flow resistance and pollutant abatement. The CFD analysis was conducted by means of numerical simulations carried out on a geometry sample obtained by the micro-tomography technique to investigate the flow regime type and to extract pressure drop information. The result of this analysis was used to set guideline for the design of foam type substrate and to provide a first estimation of cost effectiveness of new type of substrates.