With　the　growth　of　industrialization,　the　global　manufacturing　industry　is　continually　evolving　and　reforming　in　the　direction　of　intelligence　and　green　production.　Industrial　robots　have　replaced　human　workers　because　of　the　benefit　of　production　efficiency.　However,　the　large-scale　application　of　robots　requires　a　large　amount　of　energy　consumption　and　generates　a　large　amount　of　CO　2　,　which　will　lead　to　energy　waste　and　environmental　pollution.　In　addition,　in　term　of　performing　some　particular　tasks,　current　robot　technology　cannot　achieve　the　same　level　of　intelligence　as　human.　Therefore,　the　design　trend　of　assembly　lines　in　industry　has　shifted　from　traditional　configuration　to　human-robot　collaboration　to　achieve　higher　productivity　and　flexibility.　This　paper　investigates　the　human-robot　collaboration　(HRC)　assembly　line　balancing　problem,　taking　cycle　time　and　carbon　emission　as　primary　and　secondary　objectives.　A　new　mixed-integer　programming　model　that　features　a　cross-station　design　is　formulated.　A　particle　swarm　algorithm　(PSO)　with　two　improvement　rules　is　designed　to　solve　the　problems.　The　comparative　experiments　on　ten　benchmark　datasets　are　conducted　to　assess　the　performance　of　the　proposed　algorithm.　The　experimental　results　indicate　that　the　improved　particle　swarm　algorithm　is　superior　to　the　other　two　heuristics:　simulated　annealing　(SA)　and　the　late　acceptance　hill-climbing　heuristic　(LAHC).