The rise of metamorphic malware, a dangerous type of malware, has sparked growing research interest due to its increasing attacks on information assets and computer networks. Sophos’ recent threat report reveals that 94% of malware targeting organizations are either metamorphic or polymorphic, highlighting the need for more research into these complex malicious groups. Metamorphic malware alters its code with each execution, making it challenging to detect using traditional methods. As a step to address this, this paper employs a Multi-Objective Evolutionary Algorithm (MO-EA) in an adversarial learning setting to generate a large and evasive archive of mutants of malware to serve as training data in detecting metamorphic malware. The experimental results show that MO-EA, when tested on a personal information stealing malware, generated an evasive archive of mutants that evaded 60% to 73% of 63 detection engines. Compared to other approaches that employ a Single Objective EA and Quality Diversity EA, MO-EA offers a more evasive range of solutions and thus a more robust archive that can serve as training data for machine learning models in detecting metamorphic malware.