Due　to　the　limited　computing　capacity　of　onboard　devices,　they　can　no　longer　meet　a　large　number　of　computing　requirements.　Therefore,　mobile　edge　computing　(MEC)　provides　more　computing　and　storage　capabilities　for　vehicles.　Inspired　by　a　large　number　of　roadside　parking　vehicles,　this　paper　takes　the　roadside　parking　vehicles　with　idle　computing　resources　as　the　task　offloading　platform　and　proposes　a　mobile　edge　computing　task　offloading　strategy　based　on　roadside　parking　cooperation.　The　resource　sharing　and　mutual　utilization　among　roadside　vehicles,　roadside　units　(RSU),　and　cloud　servers　(cloud　servers)　were　established,　and　the　collaborative　offloading　problem　of　computing　tasks　was　transformed　into　a　constraint　problem.　The　hybrid　genetic　algorithm　(HHGA)　with　a　mountain-climbing　operator　was　used　to　solve　the　multi-constraint　problem,　to　reduce　the　delay　and　energy　consumption　of　computing　tasks.　The　simulation　results　show　that　when　the　number　of　tasks　is　25,　the　delay　and　energy　consumption　of　the　HHGA　algorithm　is　improved　by　24.1%　and　11.9%,　respectively,　compared　with　Tradition.　When　the　task　size　is　1.0　MB,　the　HHGA　algorithm　reduces　the　system　overhead　by　7.9%　compared　with　Tradition.　Therefore,　the　proposed　scheme　can　effectively　reduce　the　total　system　cost　during　task　offloading.