Risk　Control　is　of　great　importance　for　software　project　management.　However,　the　software　risk　control　currently　depends　on　the　manager＇s　subjective　judgments,　and　lacks　quantitative　supporting　tools.　In　this　paper　a　software　risk　control　optimization　model　is　presented.　By　forming　project　strategy　and　increasing　activities＇　costs,　the　objective　of　maximizing　the　expected　value　of　project＇s　capital　investment　is　achieved.　Moreover,　because　the　model　is　a　NLP,　a　particle　swarm　algorithm　approach　is　proposed.　Finally　an　example　is　utilized　to　verify　the　effectiveness　of　this　method.　The　research　provides　strong　supports　for　effectively　managing　and　quantitatively　controlling　software　risk,　control　activities　And　reduce　the　probability　of　failure　project,　maximize　the　project　expected　revenue.　©2009　IEEE.