Parallelization　of　legacy　programs　is　on　huge　demand　as　multi-core　platforms　are　pervasively　adopted　in　many　sectors　of　industry.　In　order　to　parallelize　a　legacy　program,　two　steps　must　be　done:　parallelism　discovery　and　parallelization　planning.　Parallelism　discovery　is　the　process　of　identifying　code　regions　where　have　exploitable　parallelism.　And　for　parallelization　planning,　many　aspects　should　be　considered　including　but　not　limited　to　the　speedup,　core　load　balancing,　communication　costs,　etc.　In　this　work,　we　model　the　parallelization　planning　as　a　multi-objective　optimization　problem.　A　genetic　algorithm　is　developed　to　solve　this　multi-objective　optimization　problem　by　evaluating　the　solution　set　taking　consideration　of　all　the　above　aspects.　We　have　tested　our　approach　on　the　real　industrial　applications　to　validate　its　feasibility　and　efficiency.　©　2019　IEEE.