A computational intelligence-based identification of the properties of maximally sustainable materials for a given application, as derived from key properties of existing candidate materials, is put forward. The correlation surface between material properties (input) and environmental impact (EI) values (output) of the candidate materials is initially created using general regression (GR) artificial neural networks (ANNs). Genetic algorithms (GAs) are subsequently employed for swiftly identifying the minimum point of the correlation surface, thus exposing the properties of the maximally sustainable material. The ANN is compared to and found to be more accurate than classic polynomial regression (PR) interpolation/prediction, with sensitivity and multicriteria analyses further confirming the stability of the proposed methodology under variations in the properties of the materials as well as the relative importance values assigned to the input properties. A nominal demonstration concerning material selection for manufacturing maximally sustainable liquid containers is presented, showing that by appropriately picking the pertinent input properties and the desired material selection criteria, the proposed methodology can be applied to a wide range of material selection tasks.