Most residential buildings in European seismic-prone regions were constructed prior to the implementation of modern seismic design standards. Retrofitting these buildings to address structural vulnerabilities is crucial for meeting societal needs and enhancing community resilience. Recent earthquakes have underscored the need to consider not only the structural safety but also economic losses and the downtime resulting from seismic events in retrofitting design considerations. Furthermore, with the increasing emphasis on sustainability, the environmental impact of retrofitting solutions becomes ever more a crucial aspect in decision-making, alongside installation costs. In contrast to many existing studies that consider a limited set of retrofitting options, this study employs an optimisation algorithm to explore a broader range of solutions. A Genetic Algorithm (GA), which incorporates a novel Stochastic Iterative Retrofitting Algorithm (SIRA) for generating initial populations, is utilised to explore various retrofitting techniques, including concrete jacketing, steel jacketing, and fibre-reinforced polymer (FRP) wrapping. While a reinforced concrete (RC) infilled building was selected as a case study herein, the methodology was designed to be broadly applicable, allowing for its implementation across various building layouts and retrofitting strategies. The retrofitting solutions were evaluated, targeting multiple objectives based on different variables, including installation cost, environmental impact, expected annual loss, and downtime, while ensuring compliance with baseline code requirements. The results demonstrate the potential of optimisation algorithms in helping decision-makers identify the most effective retrofitting solutions.