For　objects　with　arbitrary　angles　in　optical　remote　sensing　(RS)　images,　the　oriented　bounding　box　regression　task　often　faces　the　problem　of　ambiguous　boundaries　between　positive　and　negative　samples.　The　statistical　analysis　of　existing　label　assignment　strategies　reveals　that　anchors　with　low　Intersection　over　Union　(IoU)　between　ground　truth　(GT)　may　also　accurately　surround　the　GT　after　decoding.　Therefore,　this　article　proposes　an　attention-based　mean-max　balance　assignment　(AMMBA)　strategy,　which　consists　of　two　parts:　mean-max　balance　assignment　(MMBA)　strategy　and　balance　feature　pyramid　with　attention　(BFPA).　MMBA　employs　the　mean-max　assignment　(MMA)　and　balance　assignment　(BA)　to　dynamically　calculate　a　positive　threshold　and　adaptively　match　better　positive　samples　for　each　GT　for　training.　Meanwhile,　to　meet　the　need　of　MMBA　for　more　accurate　feature　maps,　we　construct　a　BFPA　module　that　integrates　spatial　and　scale　attention　mechanisms　to　promote　global　information　propagation.　Combined　with　S2ANet,　our　AMMBA　method　can　effectively　achieve　state-of-the-art　performance,　with　a　precision　of　80.91%　on　the　DOTA　dataset　in　a　simple　plug-and-play　fashion.　Extensive　experiments　on　three　challenging　optical　RS　image　datasets　(DOTA-v1.0,　HRSC,　and　DIOR-R)　further　demonstrate　the　balance　between　precision　and　speed　in　single-stage　object　detectors.　Our　AMMBA　has　enough　potential　to　assist　all　existing　RS　models　in　a　simple　way　to　achieve　better　detection　performance.　The　code　is　available　at　https://github.com/promisekoloer/AMMBA.