An　information　geometric　algorithm　is　proposed　to　control　the　shape　of　the　conditional　output　probability　density　function　for　stochastic　distribution　control　systems.　The　considered　system　is　of　multi-input　and　multi-output　with　stochastic　noises　and　a　output　feedback　vector.　Based　on　the　assumption　that　the　probability　density　function　of　the　stochastic　noise　is　known,　we　obtain　the　conditional　output　probability　density　function　using　the　probability　theories,　whose　shape　can　be　considered　to　be　determined　by　the　control　input　vector　and　the　output　feedback　vector.　The　set　of　the　conditional　output　probability　density　function　forms　a　manifold(M),　and　the　control　input　and　the　output　feedback　can　be　considered　as　the　coordinate　system　from　the　viewpoint　of　information　geometry.　The　Kullback-Leibler　divergence　acts　as　the　distance　between　the　conditional　output　probability　density　function　and　the　target　probability　density　function,　and　can　be　considered　as　an　error　function.　For　the　output　feedback　vector　is　known,　our　propose　is　to　design　the　control　input　vector　to　make　the　conditional　output　probability　density　function　as　close　as　possible　to　the　given　one.　Thus,　an　iterative　formula　for　the　control　input　vector　is　proposed　in　the　sense　of　information　geometry.　Finally,　an　illustrative　example　is　utilized　to　demonstrate　the　effectiveness　of　the　algorithm.