To　improve　the　fuel　economy　of　a　plug-in　hybrid　electric　vehicle　(PHEV)　in　charge-sustaining　mode,　and　to　solve　the　problem　regarding　the　effect　of　power　component　efficiency　on　system　energy　utilization　when　the　PHEV　is　running,　an　optimal　system　efficiency　control　strategy　was　implemented.　Based　on　the　test　data　of　the　PHEV　key　power　components,　a　numerical　model　of　the　efficiency　of　key　components,　such　as　the　engine,　driving　motor,　continuously　variable　transmission,　and　power　battery,　was　established.　Then,　the　temperature　and　battery　state　of　charge　(SOC)　with　an　effect　on　the　charge　and　discharge　power　were　considered.　The　optimal　hybrid　system　efficiency　served　as　a　fitness　function,　and　the　CVT　ratio　and　engine　torque　served　as　optimization　variables.　The　speed,　acceleration,　and　SOC　were　considered　as　the　state　variables.　The　performance　indicators　were　taken　as　the　constraints.　Then,　iterative　optimization　was　carried　out　by　a　genetic　algorithm　(GA).　The　system　efficiency　converged　to　the　global　optimum　in　the　20th　generation.　The　CVT　ratio　and　engine　torque　also　converged　to　the　optimum　value　by　genetic　evolutions　through　a　series　of　generations.　The　results　of　the　optimal　control　strategy　and　the　vehicle　speed　and　acceleration　were　fitted　into　the　corresponding　three-dimensional　control　tables.　By　integrating　a　numerical　modeling　method　with　an　experimental　data　modeling　method,　a　strategy　simulation　model　of　the　vehicle　was　developed　based　on　the　MATLAB/Simulink　software,　and　a　simulation　analysis　was　carried　out　in　the　new　European　driving　cycle.　The　results　reveal　that,　in　comparison　with　the　optimal　system　efficiency　control　strategy　by　the　GA　in　the　in-charge-sustaining　mode　of　the　PHEV,　the　battery　SOC　operation　is　more　reasonable,　the　efficiency　of　the　CVT　improves　steadily,　and　the　torque　between　the　engine　and　the　motor　is　more　reasonably　distributed.　The　fuel　consumption　per　100　km　is　reduced　from　5.2　L　to　4.5　L,　while　the　fuel　economy　increases　by　13.5%　in　comparison　with　the　fuel　economy　before　optimization.　©　2018,　Editorial　Department　of　China　Journal　of　Highway　and　Transport.　All　right　reserved.