Bridging　the　gap　between　academia　and　industry　is　an　important　issue　to　promote　the　practicality　of　i*　framework.　Researchers　have　been　dealing　with　this　issue　from　various　perspectives,　such　as　simplifying　the　meta-models　or　modeling　processes　of　i*　framework.　In　this　paper,　we　exclusively　focus　on　the　scalability　issue　in　laying　out　large-scale　i*　models　and　propose　a　two-level　layout　approach　to　automatically　lay　out　i*　models　in　an　efficient　and　comprehensible　manner,　contributing　to　the　adoption　of　i*　framework　in　the　industry.　The　proposed　approach　is　designed　by　considering　the　semantics　of　i*　constructs　and　layout　conventions　of　i*　models　in　order　to　produce　meaningful　layouts　and　can　appropriately　handle　both　the　SD　(Strategic　Dependency)　view　and　the　SR　(Strategic　Rationale)　view　of　i*　models.　We　have　implemented　our　approach　in　an　open-access　prototype　tool,　which　is　able　to　be　integrated　with　existing　iStarML-compatible　modeling　tools.　We　have　conducted　a　controlled　experiment,　a　case　study,　and　performance　testing　to　empirically　and　comprehensively　evaluate　the　utility　of　our　approach,　the　results　of　which　show　that　our　proposal　can　efficiently　produce　meaningful　layouts　that　are　as　comprehensible　as　manually　laid　out　models　in　most　cases.