The need to produce more sustainable concrete is proving imminent given the rising environmental concerns facing the industry. Blended cement concrete, based on any of the prominent supplementary cementitious materials (SCMs) such as fly ash, ground granulated blast-furnace slag, silica fume, calcined clay and limestone powder, have proven to be the best candidates for sustainable concrete mixes. However, a reliable sustainability measure includes not only the environmental impact, but also the economic and functional ones. Within these five SCMs, their environmental, economic and functional properties are found to be conflicting at times, making a clear judgement on what would be the optimum mix not a straightforward path. A recent framework and tool for concrete sustainability assessment ECO2, sets a reliable methodology for including the functional performance of a concrete mix depending on project-based specifications. Therefore, in this study, a recently published regression model, Pre-bcc was used to predict the functional properties of a wide grid search of potentially suitable blended cement concrete mixes. Hence, an open access novel genetic algorithm tool "Opt-bcc" was developed and used to optimize the sustainability score of these mixes based on a set selection of user-defined project-specific functional criteria. The optimized mixes using the Opt-bcc model for each strength class were compared against the mix design proposed by other optimization models from the literature and were found to be at least 70% cheaper and of 30% less environmental impact.