Multicore　platforms　are　pervasively　deployed　in　many　different　sectors　of　industry.　Hence,　it　is　appealing　to　accelerate　the　execution　through　adapting　the　sequential　programs　to　the　underlying　architecture　to　efficiently　utilize　the　hardware　resources,　e.g.,　the　multi-cores.　However,　the　parallelization　of　legacy　sequential　programs　remains　a　grand　challenge　due　to　the　complexity　of　the　program　analysis　and　dynamics　of　the　runtime　environment.　This　paper　focuses　on　parallelization　planning　in　that　the　best　parallelization　candidates　would　be　determined　after　the　parallelism　discovery　in　the　target　large　sequential　programs.　In　this　endeavor,　a　genetic　algorithm　based　method　is　deployed　to　help　find　an　optimal　solution　considering　different　aspects　from　the　task　decomposition　to　solution　evaluation　while　achieving　the　maximized　speedup.　We　have　experimented　the　proposed　approach　on　industrial　real　time　embedded　application　to　reveal　excellent　speedup　results.