To　solve　the　unilateral　problems　of　job-shop　scheduling　performance　measures　used　in　previous　studies,　by　using　an　unified　measure-profit　integrated　time,　quality,　cost,　energy　and　environment　along　with　entropy　as　evaluating　the　scheduling　effectiveness,　an　approach　for　job-shop　scheduling　algorithm　is　proposed　based　on　genetic　algorithm.　Wherein,　the　profit　is　regarded　as　the　adaptive　value　of　the　chromosome　in　genetic　algorithm,　a　set　of　suboptimal　scheduling　schemes　gained　via　searches,　and　the　best　scheduling　scheme　is　gained　after　decision-making　for　the　suboptimal　scheduling　schemes　with　profit　and　entropy　as　objectives　summed　up　by　entropic　weights.　Examples　and　programs　in　program　language　C#　are　used　to　illustrate　validation.　It　shows　that　the　proposed　approach　have　some　advantage　than　that　of　traditional　scheduling　algorithms　in　all　sidedness　and　practicability　of　performance　measures.