In-vehicle　network　security　plays　a　vital　role　in　ensuring　the　secure　information　transfer　between　vehicle　and　Internet.　The　existing　research　is　still　facing　great　difficulties　in　balancing　the　conflicting　factors　for　the　in-vehicle　network　security　and　hence　to　improve　intrusion　detection　performance.　To　challenge　this　issue,　we　construct　a　many-objective　intrusion　detection　model　by　including　information　entropy,　accuracy,　false　positive　rate　and　response　time　of　anomaly　detection　as　the　four　objectives,　which　represent　the　key　factors　influencing　intrusion　detection　performance.　We　then　design　an　improved　intrusion　detection　algorithm　based　on　many-objective　optimization　to　optimize　the　detection　model　parameters.　The　designed　algorithm　has　double　evolutionary　selections.　Specifically,　an　improved　differential　evolutionary　operator　produces　new　offspring　of　the　internal　population,　and　a　spherical　pruning　mechanism　selects　the　excellent　internal　solutions　to　form　the　selected　pool　of　the　external　archive.　The　second　evolutionary　selection　then　produces　new　offspring　of　the　archive,　and　an　archive　selection　mechanism　of　the　external　archive　selects　and　stores　the　optimal　solutions　in　the　whole　detection　process.　An　experiment　is　performed　using　a　real-world　in-vehicle　network　data　set　to　verify　the　performance　of　our　proposed　model　and　algorithm.　Experimental　results　obtained　demonstrate　that　our　algorithm　can　respond　quickly　to　attacks　and　achieve　high　entropy　and　detection　accuracy　as　well　as　very　low　false　positive　rate　with　a　good　trade-off　in　the　conflicting　objective　landscape.