As　a　key　technology　to　perform　sustainable　development　in　transportation,　electric　vehicles　have　been　largely　welcomed　due　to　their　advantages　in　energy　savings　and　low　carbon　emission.　The　main　step　in　promoting　these　vehicles　is　the　selection　of　the　appropriate　electric　vehicle　charging　station　(EVCS)　site.　EVCS　site　selection　is　a　laborious　task　because　it　involves　a　series　of　conflicting　quantitative　and　qualitative　criteria　from　several　dimensions.　The　quantitative　criteria　are　usually　expressed　by　numerical　data,　while　qualitative　criteria　are　commonly　represented　by　linguistic　terms.　Furthermore,　the　linguistic　terms　generated　by　different　decision　makers　are　usually　defined　on　multigranular　linguistic　term　sets.　In　this　article,　we　present　a　new　decision-making　framework　to　select　sustainable　EVCS　sites　within　the　context　of　heterogeneous　information　and　multigranular　linguistic　terms.　First,　three　information　transformation　mechanisms　are　defined　to　unify　the　heterogeneous　information　and　multigranular　linguistic　terms　into　interval-valued　belief　structures.　Afterward,　shadowed　sets　theory　is　utilized　to　reflect　the　personalized　individual　semantics　of　linguistic　terms.　Then,　with　the　aid　of　the　evidential　reasoning　(ER)　algorithm,　a　new　information　fusion　method　is　proposed　to　generate　the　interval-valued　expected　utilities　of　alternatives.　Subsequently,　an　improved　minimax　regret　approach　is　developed　to　compare　and　rank　the　interval-valued　expected　utilities.　The　proposed　decision-making　framework　is　then　implemented　to　solve　a　case　study　for　EVCS　site　selection.　Further　analysis　and　comparisons　with　other　methods　are　also　conducted　to　show　the　applicability　and　feasibility　of　the　current　proposal.