Remarkable　trade-off　paradoxes　widely　exist　among　different　magnetic　properties　of　permanent　magnetic　alloys,　especially　between　saturation　magnetization　and　coercivity,　largely　impeding　the　improvement　of　comprehensive　properties.　Taking　Sm-Co-based　alloys　as　an　example,　this　study　proposed　a　new　data-driven　material　design　strategy　to　dissolve　the　saturation　magnetization-coercivity　trade-off　and　enhance　the　comprehensive　magnetic　properties.　The　machine　learning　approach　and　multi-objective　optimization　method　were　applied　to　establish　a　model　for　composition　design　and　microstructure　regulation　to　simultaneously　maximize　saturation　magnetization　and　coercivity.　It　was　found　that　the　electronegativity　of　the　doping　element　is　a　key　feature　that　affects　both　the　saturation　magnetization　and　coercivity,　and　the　Pareto　front　with　appropriate　alloy　composition　and　grain　size　was　obtained.　The　materials　with　best　comprehensive　magnetic　properties　in　the　optimal　set　were　selected　for　experimental　preparation,　and　the　results　fully　verified　the　model　predictions.　The　machine　learning　model　and　multi-objective　optimization　method　established　in　this　study　break　through　the　trade-off　between　saturation　magnetization　and　coercivity　of　Sm-Co-based　alloys,　and　the　strategy　for　synergistic　improvement　of　the　mutually　exclusive　properties　is　appliable　to　a　variety　of　multi-objective　materials　design　issues.