With　the　rapid　development　of　Internet　of　Vehicles,　computing-intensive　and　delay-sensitive　applications　are　widely　used.　Faced　with　the　shortcomings　of　less　computing　resources　and　limited　power　supply　of　vehicle　mobile　terminals,　mobile　edge　technology　came　into　being.　Firstly,　a　multi-terminal　single-edge　vehicle　network　model　is　established　to　alleviate　the　terminal　pressure　by　accessing　Multi-Access　Edge　Computing　(MEC)　servers.　Aiming　at　the　heterogeneous　computing　network,　this　paper　constructs　a　joint　optimization　problem　of　task　offloading　and　resource　allocation　under　the　constraints　of　delay　and　energy　consumption.　Aiming　at　the　problem　that　the　resources　in　the　offloading　decision　process　exceed　the　load,　the　modified　chromosome　and　the　improved　elite　selection　strategy　are　used　to　optimize　the　genetic　algorithm.　The　dynamic　parameter　adjustment　strategy　is　used　to　optimize　the　particle　swarm　optimization　algorithm　to　prevent　premature　convergence.　A　two-stage　joint　optimization　algorithm　is　proposed　to　solve　the　problem.　It　can　be　seen　from　the　results　that　the　optimized　algorithm　can　find　the　appropriate　optimal　solution　and　effectively　reduce　the　cost　compared　with　GAVECOS　and　Partial　algorithm.　©　2023　SPIE.