The　construction　of　timing-driven　Steiner　minimum　tree　is　a　critical　issue　in　VLSI　routing　design.　Meanwhile,　since　the　interconnection　model　of　X-architecture　can　make　full　use　of　routing　resources　compared　to　the　traditional　Manhattan　architecture,　constructing　a　Timing-Driven　X-architecture　Steiner　Minimum　Tree　(TDXSMT)　is　of　great　significance　to　improving　routing　performance.　In　this　paper,　an　efficient　algorithm　based　on　Social　Learning　Multi-Objective　Particle　Swarm　Optimization　(SLMOPSO)　is　proposed　to　construct　a　TDXSMT　with　minimizing　the　maximum　source-to-sink　pathlength.　An　X-architecture　Prim-Dijkstra　model　is　presented　to　construct　an　initial　Steiner　tree　which　can　optimize　both　the　wirelength　and　the　maximum　source-to-sink　pathlength.　In　order　to　find　a　better　solution,　an　SLMOPSO　method　based　on　the　nearest　and　best　select　strategy　is　presented　to　improve　the　global　exploration　capability　of　the　algorithm.　Besides,　the　mutation　and　crossover　operators　are　utilized　to　achieve　the　discrete　particle　update　process,　thereby　better　solving　the　discrete　TDXSMT　problem.　The　experimental　results　indicate　that　the　proposed　algorithm　has　an　excellent　trade-off　between　the　wirelength　and　maximum　source-to-sink　pathlength　of　the　routing　tree　and　can　greatly　optimize　the　timing　delay.