The　driving　strategy　of　the　driver　is　one　of　the　critical　factors　which　affect　the　energy　consumption　of　the　vehicle.　However,　it　is　often　overlooked　in　the　researches　of　energy　management.　Moreover,　there　are　few　studies　devoted　to　formulating　specific　driving　strategies　which　consider　the　driving　preference　of　the　driver.　This　study　proposes　a　multi-objective　optimized　driving　strategy　for　dual-motor　electric　vehicles　based　on　the　intelligent　algorithms,　which　mainly　studies　the　flat　road　sections,　the　long　uphill　road　sections,　and　the　vehicle　acceleration　sections.　The　acceleration　duration,　energy　consumption,　and　driving　comfort　are　defined　as　the　optimized　objectives.　For　the　economy　performance,　the　optimized　driving　strategy　designs　the　travel　speed　trajectory　according　to　the　given　road　information　to　guide　and　adjust　the　driving　behavior,　so　that　the　vehicle　can　be　driven　economically.　In　the　process　of　studying　vehicle　multi-objective　acceleration　strategy,　NSGA-II,　MOPSO,　PESA-II　and　SPEA2　were　adopted　to　solve　the　multi-objective　optimization　problem　with　three　conflicting　objectives.　And　the　performance　comparison　and　statistical　analysis　of　the　four　algorithms　were　carried　out.　Finally,　a　spliced　driving　cycle　was　constructed　to　validate　the　effectiveness　of　the　driving　strategy.　The　results　indicated　that　the　proposed　driving　strategy　has　an　excellent　performance　towards　driving　assistance.　©　2021　Elsevier　B.V.