The　capacitated　minimum　spanning　tree　(CMST)　problem　is　a　fundamental　problem　in　telecommunication　network　design.　Given　a　central　node　and　a　set　of　remote　terminal　nodes　with　specified　demands　for　traffic,　the　goal　of　CMST　is　to　find　a　minimum　cost　spanning　tree　(network)　such　that　the　traffic　on　any　arc　of　the　network　satisfies　the　capacity　constraint.　To　tackle　this　NP-hard　problem,　we　propose　an　effective　hybrid　evolutionary　algorithm　that　integrates　a　backbone-based　crossover　operator,　a　destroy-and-repair　mutation　operator　to　generate　meaningful　offspring　solutions,　and　an　intensification-driven　adaptive　tabu　search　procedure　that　considers　both　feasible　and　infeasible　solutions　in　search　of　high-quality　local　optima.　Extensive　computational　results　on　a　set　of　126　well-known　CMST　benchmark　instances　from　the　literature　indicate　that　the　proposed　algorithm　competes　favorably　with　the　state-of-the-art　heuristics.　For　a　selection　of　25　most　challenging　CMST　instances　with　unknown　optimal　solutions,　the　proposed　algorithm　reports　new　upper　bounds　(improved　best-known　solutions)　in　9　cases,　while　reaching　the　best-known　result　for　12　instances.　Furthermore,　we　provide　experimental　analyses　to　identify　the　key　algorithmic　features　of　the　proposed　approach.