For　a　CPU-GPU　heterogeneous　computing　system,　different　types　of　processors　have　load　balancing　problems　in　the　calculation　process.　What＇s　more,　multitasking　cannot　be　matched　to　the　appropriate　processor　core　is　also　an　urgent　problem　to　be　solved.　In　this　paper,　we　propose　a　task　scheduling　strategy　for　high-performance　CPU-GPU　heterogeneous　computing　platform　to　solve　these　problems.　For　the　single　task　model,　a　task　scheduling　strategy　based　on　load-aware　for　CPU-GPU　heterogeneous　computing　platform　is　proposed.　This　strategy　detects　the　computing　power　of　the　CPU　and　GPU　to　process　specified　tasks,　and　allocates　computing　tasks　to　the　CPU　and　GPU　according　to　the　perception　ratio.　The　tasks　are　stored　in　a　bidirectional　queue　to　reduce　the　additional　overhead　brought　by　scheduling.　For　the　multi-task　model,　a　task　scheduling　strategy　based　on　the　genetic　algorithm　for　CPU-GPU　heterogeneous　computing　platform　is　proposed.　The　strategy　aims　at　improving　the　overall　operating　efficiency　of　the　system,　and　accurately　binds　the　execution　relationship　between　different　types　of　tasks　and　heterogeneous　processing　cores.　Our　experimental　results　show　that　the　scheduling　strategy　can　improve　the　efficiency　of　parallel　computing　as　well　as　system　performance.