To address the challenges of finding optimal paths in continuous spaces with graph-based planning algorithms and the low efficiency of path generation in sampling-based algorithms, this study proposes an optimal path planning algorithm based on informed sampling of convex dissection(CDI-RRT*). First, the algorithm performs convex dissection on a static map and establishes a topological graph. Guided by this graph, it uses the A* algorithm to generate an initial path, which is then optimized with an elastic band algorithm to obtain a locally optimal path. Subsequently, the CDI-RRT* constructs an initial tree within the topological framework and incorporates constraints from both convex edges and the informed set of the Informed-RRT* algorithm to build a dynamic sampling domain. By performing random sampling within this domain, the initial tree is iteratively optimized to yield an optimal path. Finally, a comparative study of the CDI-RRT* and current state-of-the-art optimal path planning algorithms is conducted through simulations and real-world experiments. Results demonstrate that the CDI-RRT* outperforms in terms of initial and optimal path generation efficiency in most scenarios, validating its feasibility and effectiveness.