We　propose　a　consensus　opinion　model　based　on　the　evolutionary　game.　In　our　model,　both　of　the　two　connected　agents　receive　a　benefit　if　they　have　the　same　opinion,　otherwise　they　both　pay　a　cost.　Agents　update　their　opinions　by　comparing　payoffs　with　neighbors.　The　opinion　of　an　agent　with　higher　payoff　is　more　likely　to　be　imitated.　We　apply　this　model　in　scale-free　networks　with　tunable　degree　distribution.　Interestingly,　we　find　that　there　exists　an　optimal　ratio　of　cost　to　benefit,　leading　to　the　shortest　consensus　time.　Qualitative　analysis　is　obtained　by　examining　the　evolution　of　the　opinion　clusters.　Moreover,　we　find　that　the　consensus　time　decreases　as　the　average　degree　of　the　network　increases,　but　increases　with　the　noise　introduced　to　permit　irrational　choices.　The　dependence　of　the　consensus　time　on　the　network　size　is　found　to　be　a　power-law　form.　For　small　or　larger　ratio　of　cost　to　benefit,　the　consensus　time　decreases　as　the　degree　exponent　increases.　However,　for　moderate　ratio　of　cost　to　benefit,　the　consensus　time　increases　with　the　degree　exponent.　Our　results　may　provide　new　insights　into　opinion　dynamics　driven　by　the　evolutionary　game　theory.　©　Copyright　2016　EPLA.