A　hybrid　genetic　simulated　annealing　algorithm　is　presented　for　solving　the　problem　of　VLSI　standard　cell　placement　with　up　to　millions　of　cells.　Firstly,　to　make　genetic　algorithm　be　capable　of　handling　very　large　scale　of　standard　cell　placement,　the　strategies　of　small　size　population,　dynamic　updating　population,　and　crossover　localization　are　adopted,　and　the　global　search　and　local　search　of　genetic　algorithm　are　coordinated.　Then,　by　introducing　hill　climbing　(HC)　and　simulated　annealing　(SA)　into　the　framework　of　genetic　algorithm　and　the　internal　procedure　of　its　operators,　an　effective　crossover　operator　named　Net　Cycle　Crossover　and　local　search　algorithms　for　the　placement　problem　are　designed　to　further　improve　the　evolutionary　efficiency　of　the　algorithm　and　the　quality　of　its　placement　results.　In　the　algorithm　procedure,　HC　method　and　SA　method　focus　on　array　placement　and　non-array　placement　respectively.　The　experimental　results　on　Peko　suite3,　Peko　suite4　and　ISPD04　benchmark　circuits　show　that　the　proposed　algorithm　can　handle　array　and　non-array　placements　with　10,000~1,600,000　cells　and　10,000~210,000　cells　respectively,　and　can　effectively　improve　the　quality　of　placement　results　in　a　reasonable　running　time.