Genetic　algorithm　and　tabu　search　algorithm　are　powerful　tools　to　solve　the　complicated　large-scale　optimization　problems.　Through　comprehensive　contrast　and　comparison　between　the　above　two　algorithms,　a　hybrid　optimization　algorithm　was　proposed　to　improve　the　local　search　ability　of　genetic　algorithm.　In　this　algorithm,　in　order　to　speed　up　convergence　speed　and　get　satisfied　results,　tabu　search　algorithm　was　applied　for　local　search,　and　genetic　algorithm　was　used　for　global　search.　Meanwhile　a　strategy　was　proposed　to　control　prematurity　and　to　avoid　converging　to　local　optimum.　The　test　results　show　that　both　calculating　speed　and　output　are　improved.