To　achieve　the　purpose　of　large-scale　visual　tracking　of　targets　based　on　Visual　Sensor　Networks　(VSNs),　this　paper　proposes　a　sensor　placement　optimization　method　to　provide　a　suitable　sensor　configuration　that　satisfies　the　requirements　of　maximal　network　coverage　and　minimal　reconstruction　error　as　closely　as　possible.　In　the　proposed　method,　some　necessary　models　are　established　as　the　mathematical　basis　of　the　optimization　process.　Then,　a　multi-objective　optimization　function　is　designed　to　balance　the　coverage　and　reconstruction　error,　followed　by　an　intelligent　optimization　algorithm　that　combines　the　advantages　of　Particle　Swarm　Optimization　(PSO)　and　Genetic　Algorithm　(GA).　Additionally,　a　dimensionality　reduction　technique　is　used　to　represent　the　sensor　location　information　using　a　one-dimensional　forward　distance,　which　makes　the　search　process　simpler　and　increases　execution　speed.　Finally,　comprehensive　comparison　experiments　are　carried　out　in　various　settings　to　show　the　reliability　and　effectiveness　of　the　optimization　algorithms.　©　2023　Technical　Committee　on　Control　Theory,　Chinese　Association　of　Automation.