With　the　development　of　information　technology,　mobile　edge　computing　is　regarded　as　a　promising　technology　by　pushing　more　computing　resources　to　the　edge　of　the　network　to　meet　the　requirements　of　mobile　applications.　However,　reliable　and　efficient　computation　offloading　in　high　mobility　scenarios　such　as　expressways　and　high-speed　trains　still　remains　a　challenge.　In　this　paper,　we　take　the　user＇s　high　mobility　characteristics　into　consideration　and　propose　a　mobility-aware　predictive　computation　offloading　and　task　scheduling　scheme　based　on　genetic　algorithm　to　solve　the　users＇　task　offloading　and　scheduling　problem　in　high　mobility　scenarios.　To　reduce　the　offloading　failure　rate　of　the　high　mobility　users,　we　put　forward　a　prediction　module　to　predict　the　users＇　speed　and　estimate　the　user＇s　residence　time　to　assist　the　offloading　decision.　Based　on　the　prediction　information,　we　formulate　the　computation　offloading　and　task　scheduling　problem　as　a　combinational　optimization　problem,　and　utilize　a　two-layer　coding　genetic　algorithm　to　optimize　the　offloading　decision　and　scheduling　sequence　of　the　high　mobility　users.　Simulation　results　show　that　the　proposed　algorithm　can　effectively　reduce　the　offloading　failure　rate　and　is　superior　to　other　traditional　algorithms　in　performance　improvement.　©　2021　IEEE.