In　this　paper,　we　propose　a　Multi-objective　Five-element　Cycle　Optimization　algorithm　based　on　Multi-strategy　fusion　(MOFECO-MS)　to　address　the　Bi-objective　Traveling　Thief　Problem　(BITTP),　an　extension　of　the　Traveling　Thief　Problem　that　incorporates　two　conflicting　objectives.　The　novelty　of　our　approach　lies　in　a　unique　individual　selection　strategy　coupled　with　an　innovative　element　update　mechanism　rooted　in　the　Five-element　Cycle　Model.　To　balance　global　exploration　and　local　exploitation,　the　algorithm　categorizes　the　population　into　distinct　groups　and　applies　crossover　operations　both　within　and　between　these　groups,　while　also　employing　a　mutation　operator　for　local　searches　on　the　best　individuals.　This　coordinated　approach　optimizes　parameter　settings　and　enhances　the　search　capabilities　of　the　algorithm.　Extensive　experiments　were　conducted　on　nine　BITTP　instances,　comparing　MOFECO-MS　against　eight　state-of-the-art　multi-objective　optimization　algorithms.　The　results　show　that　MOFECO-MS　excels　in　both　Hypervolume　(HV)　and　Spread　(SP)　indicators,　while　also　maintaining　a　high　level　of　Pure　Diversity　(PD).　Overall,　MOFECO-MS　outperformed　the　other　algorithms　in　most　instances,　demonstrating　its　superiority　and　robustness　in　solving　complex　multi-objective　optimization　problems.