Purpose:　Flexible　job-shop　scheduling　is　significant　for　different　manufacturing　industries　nowadays.　Moreover,　consideration　of　transportation　time　during　scheduling　makes　it　more　practical　and　useful.　The　purpose　of　this　paper　is　to　investigate　multi-objective　flexible　job-shop　scheduling　problem　(MOFJSP)　considering　transportation　time.　Design/methodology/approach:　A　hybrid　genetic　algorithm　(GA)　approach　is　integrated　with　simulated　annealing　to　solve　the　MOFJSP　considering　transportation　time,　and　an　external　elitism　memory　library　is　employed　as　a　knowledge　library　to　direct　GA　search　into　the　region　of　better　performance.　Findings:　The　performance　of　the　proposed　algorithm　is　tested　on　different　MOFJSP　taken　from　literature.　Experimental　results　show　that　proposed　algorithm　performs　better　than　the　original　GA　in　terms　of　quality　of　solution　and　distribution　of　the　solution,　especially　when　the　number　of　jobs　and　the　flexibility　of　the　machine　increase.　Originality/value:　Most　of　existing　studies　have　not　considered　the　transportation　time　during　scheduling　of　jobs.　The　transportation　time　is　significantly　desired　to　be　included　in　the　FJSP　when　the　time　of　transportation　of　jobs　has　significant　impact　on　the　completion　time　of　jobs.　Meanwhile,　GA　is　one　of　primary　algorithms　extensively　used　to　address　MOFJSP　in　literature.　However,　to　solve　the　MOFJSP,　the　original　GA　has　a　possibility　to　get　a　premature　convergence　and　it　has　a　slow　convergence　speed.　To　overcome　these　problems,　a　new　hybrid　GA　is　developed　in　this　paper.　©　2019,　Emerald　Publishing　Limited.