The 3D capability map model of humanoid robot based on 3D point cloud data has the disadvantage of large voxel mesh searching computation. Considering the hierarchical advantage of OcTree in 3D space subdivision, a local environment and capability map model based on Octomap was proposed. Firstly, a binary-tree-like kinematics model of NAO humanoid robot was constructed according to the joint composition, forward kinematics, inverse kinematics and rigid body coordinate transformation of NAO robot. Secondly, the forward kinematics was used to calculate the 3D discrete reachable point clouds in Cartesian space, which were used as the basic workspace of the robot terminal effector. Thirdly, the methods of transforming the point cloud space representation into Octomap space node representation, especially the probability updating method of space node, were described emphatically. Finally, an optimization method of space node updating order selection was proposed according to the geometric relationship of nodes. With this optimization method, the space optimization representation of the humanoid robot's capability map was realized efficiently. Experimental results show that compared with the original Octomap updating method, the proposed algorithm can reduce the number of space nodes by nearly 30% and improve the computional efficiency.