How　to　schedule　public　resources　to　maximize　the　coverage　of　crowds　has　always　been　a　hot　topic.　There　are　mainly　two　types　of　research.　One　is　the　scheduling　of　static　resources,　whose　coverage　efficiency　is　lower　because　the　resources　cannot　be　moved;　the　other　is　the　dynamic　scheduling　of　movable　public　resources,　but　the　existing　methods　require　additional　devices　to　collect　crowd　volume　data.　To　address　this　issue,　this　article　introduces　a　new　category　of　mobile　agents　that　can　sense　and　execute　simultaneously.　This　means　that　an　agent　can　simultaneously　perform　crowd　volume　data　collection　tasks　and　crowds　coverage　tasks　in　its　area.　Taking　these　multi-ability　agents　as　public　resources,　this　article　proposes　a　scheduling　method　called　GE-STMC　(Greedy　Execute　-　Spatial　and　Temporal　Monte-Carlo　dropout).　The　method　considers　the　spatiotemporal　correlation　between　areas　and　evaluates　the　sensing　value　and　the　executing　value　of　areas　comprehensively　to　allocate　the　agents　accordingly.　Through　the　experimental　on　the　real　data　of　Beijing　Happy　Valley,　this　method　can　get　16%　larger　crowds　volume　coverage　than　other　baseline　methods　with　the　same　number　of　agents.　©　2023　IEEE.