This　paper　presents　a　chance　constrained　information　gap　decision　model　for　multi-period　microgrid　expansion　planning　(MMEP)　considering　two　categories　of　uncertainties,　namely　random　and　non-random　uncertainties.　The　main　task　of　MMEP　is　to　determine　the　optimal　sizing,　type　selection,　and　installation　time　of　distributed　energy　resources　(DER)　in　microgrid.　In　the　proposed　formulation,　information　gap　decision　theory　(IGDT)　is　applied　to　hedge　against　the　non-random　uncertainties　of　long-term　demand　growth.　Then,　chance　constraints　are　imposed　in　the　operational　stage　to　address　the　random　uncertainties　of　hourly　renewable　energy　generation　and　load　variation.　The　objective　of　chance　constrained　information　gap　decision　model　is　to　maximize　the　robustness　level　of　DER　investment　meanwhile　satisfying　a　set　of　operational　constraints　with　a　high　probability.　The　integration　of　IGDT　and　chance　constrained　program,　however,　makes　it　very　challenging　to　compute.　To　address　this　challenge,　we　propose　and　implement　a　strengthened　bilinear　Benders　decomposition　method.　Finally,　the　effectiveness　of　proposed　planning　model　is　verified　through　the　numerical　studies　on　both　the　simple　and　practical　complex　microgrid.　Also,　our　new　computational　method　demonstrates　a　superior　solution　capacity　and　scalability.　Compared　to　directly　using　a　professional　mixed　integer　programming　solver,　it　could　reduce　the　computational　time　by　orders　of　magnitude.