Edge　computing　has　emerged　as　a　promising　paradigm　to　meet　the　increasing　demands　of　latency-sensitive　and　computationally　intensive　applications.　In　this　context,　efficient　server　deployment　and　service　placement　are　crucial　for　optimizing　performance　and　increasing　platform　profit.　This　paper　investigates　the　problem　of　server　deployment　and　service　placement　in　a　multi-user　scenario,　aiming　to　enhance　the　profit　of　Mobile　Network　Operators　while　considering　constraints　related　to　distance　thresholds,　resource　limitations,　and　connectivity　requirements.　We　demonstrate　that　this　problem　is　NP-hard.　To　address　it,　we　propose　a　two-stage　method　to　decouple　the　problem.　In　stage　I,　server　deployment　is　formulated　as　a　combinatorial　optimization　problem　within　the　framework　of　a　Markov　Decision　Process　(MDP).　We　introduce　the　Server　Deployment　with　Q-learning　(SDQ)　algorithm　to　establish　a　relatively　stable　server　deployment　strategy.　In　stage　II,　service　placement　is　formulated　as　a　constrained　Integer　Nonlinear　Programming　(INLP)　problem.　We　present　the　Service　Placement　with　Interior　Barrier　Method　(SPIB)　and　Tree-based　Branch-and-Bound　(TDB)　algorithms　and　theoretically　prove　their　feasibility.　For　scenarios　where　the　number　of　users　changes　dynamically,　we　propose　the　Distance-and-Utilization　Balance　Algorithm　(DUBA).　Extensive　experiments　validate　the　exceptional　performance　of　our　proposed　algorithms　in　enhancing　the　profit.