This　research　proposes　an　adaptive　clustering-based　genetic　algorithm　(ACGA)　to　optimize　the　pick-and-place　operation　of　a　dual-gantry　component　placement　machine,　which　has　two　independent　gantries　that　alternately　place　components　onto　a　printed　circuit　board　(PCB).　The　proposed　optimization　problem　consists　of　several　highly　interrelated　sub-problems,　such　as　component　allocation,　nozzle　and　feeder　setups,　pick-and-place　sequences,　etc.　In　the　proposed　ACGA,　the　nozzle　and　component　allocation　decisions　are　made　before　the　evolutionary　search　of　a　genetic　algorithm　to　improve　the　algorithm　efficiency.　First,　the　nozzle　allocation　problem　is　modeled　as　a　nonlinear　integer　programming　problem　and　solved　by　a　search-based　heuristic　that　minimizes　the　total　number　of　the　dual-gantry　cycles.　Then,　an　adaptive　clustering　approach　is　developed　to　allocate　components　to　each　gantry　cycle　by　evaluating　the　gantry　traveling　distances　over　the　PCB　and　the　component　feeders.　Numerical　experiments　compare　the　proposed　ACGA　to　another　clustering-based　genetic　algorithm　LCO　and　a　heuristic　algorithm　mPhase　in　the　literature　using　30　industrial　PCB　samples.　The　experiment　results　show　that　the　proposed　ACGA　algorithm　reduces　the　total　gantry　moving　distance　by　5.71%　and　4.07%　on　average　compared　to　the　LCO　and　mPhase　algorithms,　respectively.