Workforce　assignment　and　energy　consumption　impact　greatly　on　the　manufacturing　performance.　In　this　work,　we　study　a　multi-skilled　worker　assignment　and　assembly　line　balancing　problem　with　the　consideration　of　energy　consumption.　The　problem　consists　of　scheduling　products　and　assigning　workers　to　workstations　appropriately　under　a　given　cycle　time.　Two　objectives　are　minimized　simultaneously,　i.e.,　(1)　the　total　costs　including　the　processing　cost　and　the　fixed　cost　induced　by　employing　workers,　and　(2)　the　energy　consumption.　A　bi-objective　mixed-integer　linear　programming　model　is　formulated　and　an　epsilon-constraint　method　is　adopted　to　obtain　the　Pareto　front　for　small-scale　problems.　For　solving　large-size　problems,　a　processing　time　and　energy　consumption　sorted-first　rule　(PT-EC　SFR),　a　multi-objective　genetic　algorithm　(NSGA-II)　and　a　multi-objective　simulated　annealing　method　(MOSA)　are　developed.　Numerical　experiments　are　conducted　and　computational　results　show　that　the　designed　PT-EC　SFR　outperforms　the　other　two　algorithms　in　terms　of　computational　time　and　quality　of　solutions.