In　this　paper,　we　propose　a　steepest　descent　algorithm　based　on　the　natural　gradient　to　design　the　controller　of　an　open-loop　stochastic　distribution　control　system　(SDCS)　of　multi-input　and　single　output　with　a　stochastic　noise.　Since　the　control　input　vector　decides　the　shape　of　the　output　probability　density　function　(PDF),　the　purpose　of　the　controller　design　is　to　select　a　proper　control　input　vector,　so　that　the　output　PDF　of　the　SDCS　can　be　as　close　as　possible　to　the　target　PDF.　In　virtue　of　the　statistical　characterizations　of　the　SDCS,　a　new　framework　based　on　a　statistical　manifold　is　proposed　to　formulate　the　control　design　of　the　input　and　output　SDCSs.　Here,　the　Kullback-Leibler　divergence　is　presented　as　a　cost　function　to　measure　the　distance　between　the　output　PDF　and　the　target　PDF.　Therefore,　an　iterative　descent　algorithm　is　provided,　and　the　convergence　of　the　algorithm　is　discussed,　followed　by　an　illustrative　example　of　the　effectiveness.