Complex　passenger　demand　and　electricity　transmission　processes　in　metro　systems　cause　difficulties　in　formulating　optimal　timetables　and　train　speed　profiles,　often　leading　to　inefficiency　in　energy　consumption　and　passenger　service.　Based　on　energy-regenerative　technologies　and　smart-card　data,　this　study　formulates　an　optimization　model　incorporating　energy　allocation　and　passenger　assignment　to　balance　energy　use　and　passenger　travel　time.　The　Non-Dominated　Sorting　Genetic　Algorithm　II　(NSGA-II)　is　applied　and　the　core　components　are　redesigned　to　obtain　an　efficient　Pareto　frontier　of　irregular　timetables　for　maximizing　the　use　of　regenerative　energy　and　minimizing　total　travel　time.　Particularly,　a　parallelogram-based　method　is　developed　to　generate　random　feasible　timetables;　crossover　and　local-search-driven　mutation　operators　are　proposed　relying　on　the　graphic　representations　of　the　domain　knowledge.　The　suggested　approach　is　illustrated　using　real-world　data　of　a　bi-directional　metro　line　in　Beijing.　The　results　show　that　the　approach　significantly　improves　regenerative　energy　use　and　reduces　total　travel　time　compared　to　the　fixed　regular　timetable.　(C)　2020　Elsevier　Ltd.　All　rights　reserved.