Since　both　the　industry　and　the　market　have　been　driven　by　the　multi-core　processors,　to　parallelize　the　applications　in　the　automotive　industry　is　on　huge　demand.　Two　steps　are　required　to　parallelize　a　legacy　program:　parallelism　discovery　and　parallelization　planning.　To　discover　the　parallelism　of　a　program　is　to　identify　the　code　regions　where　multiple　procedures/functions　can　be　executed　simultaneously,　while　parallelization　planning　is　to　find　an　optimal　solution　of　assigning　tasks　on　multi-cores　based　on　the　discovered　parallelism.　How　to　automate　the　parallelization　in　the　Power-train　domain　remains　a　grand　challenge　due　to　the　complexity　of　the　program　and　the　dynamics　of　the　runtime　environment.　Many　aspects　should　be　considered　including　the　speedup,　computing　resource　bound,　workload　balance,　etc.　Considering　all　the　above　aspects,　we　used　a　directed　acyclic　graph　to　represent　the　decomposed　program,　then　take　the　parallelization　planning　as　a　multi-objective　optimization　problem,　where　a　Cobyla　algorithm　is　deployed　to　search　for　the　optimal　solution　by　evaluating　different　parameters.　We　have　tested　our　approach　on　the　periodic　tasks　in　the　Power-train　applications　to　validate　its　feasibility　and　efficiency.