Transient　stability　and　short-term　voltage　stability　have　successively　attracted　the　attention　of　electric　power　industry.　This　paper　proposes　a　novel　systematic　approach　for　dynamic　VAR　planning　to　improve　short-term　voltage　stability　level　and　transient　stability　level.　The　dynamic　VAR　planning　problem　is　formulated　as　a　multi-objective　optimization　(MOO)　model　with　objectives　including　investment　cost,　short-term　voltage　stability　level,　and　transient　stability　level.　To　reduce　the　complexity　of　the　proposed　MOO　model,　K-means　clustering-based　severe　contingencies　selection　and　global　sensitivity　analysis-based　potential　buses　selection　are　employed,　leading　to　a　simplified　MOO　model.　The　combination　of　a　surrogate　modeling　technique　called　support　vector　regression　and　the　multi-objective　evolutionary　algorithm　(MOEA)　are　then　used　to　solve　the　simplified　MOO　model,　considering　both　the　accuracy　of　models　and　the　optimization　computation　cost.　This　combination　makes　it　feasible　to　perform　multiple　runs　of　MOEAs　for　weakening　the　effect　of　the　MOEA＇s　randomness　to　optimal　results　and　offering　more　diverse　Pareto-optimal　solutions　for　decision　makers.　Simulations　are　carried　on　the　IEEE　39-bus　system　and　a　real　power　grid　of　China,　illustrating　that　our　methodology　is　reliable　with　high　efficiency.