In order to improve the quality and efficiency of intelligent optimization algorithms for solving path planning, the framework of knowledge-conducting intelligent optimization algorithms for solving path planning was proposed. Considering the limitaion of the knowledge adopted previously, the framework does not adopt the knowledge mined from previous iterations, which is different from the conventional conducting evolution, but adopts the costomizing domain knowledge of path planning. In order to describe the conducting manner, the intelligent optimization algorithms is defined formally as a set composed of 3 conducting objects, and the conducting manner is divided into 7 kinds of form which are separated or combined accordingly. A corresponding conducting manner is adopted according to the characteristic of different costomizing domain knowledges of path planning, and these knowledges are transformd to structured meta-strategy which can improve the performance of the algorithm. The solving framework is verified by taking particle swarm optimization algorithm for instance. Simulation experiments results have indicated that the conducting of costomizing domain knowledge can improve the algorithm's global search capabilities, and the algorithm possesses better convergence rate.