As　the　work　environment　changes　dynamically　in　real-world　manufacturing　systems,　the　dynamic　flexible　job　shop　scheduling　is　an　essential　problem　in　operations　research.　Some　works　have　taken　rescheduling　approaches　to　solve　it　as　the　multi-objective　optimization　problem.　However,　previous　studies　focus　more　on　solution　quality　improvements　while　ignoring　computation　time.　To　get　a　quick　response　in　the　dynamic　scenario,　this　paper　develops　a　fully　parallel　Non-dominated　Sorting　Genetic　Algorithm-II　(NSGA-II)　on　GPUs　and　uses　it　to　solve　the　multi-objective　dynamic　flexible　job　shop　scheduling　problem.　The　mathematical　model　is　NP-hard　which　considers　new　arrival　jobs　and　seeks　a　trade-off　between　shop　efficiency　and　schedule　stability.　The　proposed　algorithm　can　be　executed　entirely　on　GPUs　with　minimal　data　exchange　while　parallel　strategies　are　used　to　accelerate　ranking　and　crowding　mechanisms.　Finally,　numerical　experiments　are　conducted.　As　our　approach　keeps　the　original　structure　of　the　conventional　NSGA-II　without　sacrificing　the　solutions＇　quality,　it　gains　better　performance　than　other　GPU-based　parallel　methods　from　four　metrics.　Moreover,　a　case　study　of　a　large-size　instance　is　simulated　at　the　end　and　displays　the　conflicting　relationship　between　the　two　objectives.