The shortest path query is very important in the related applications of graph data. The problem studied in this work is the rule-based shortest path query, which is a special kind of shortest path problem. Given the starting vertex and the ending vertex, the rule-based shortest path query is that finding a shortest path from the starting vertex to the ending vertex, which passes through all vertices in the user-specified set, and the access order of some vertices meets certain partial order rules. It has proved that this problem is NP-hard problem. The existing work focuses on the rule-based shortest path problem on spatial datasets (the shortest distance between two vertices is expressed by Euclidean distance), which exhaustively lists all paths those satisfy the rule, and selects the path with the smallest length as the solution to the problem. However, in an actual road network, the distance between two vertices is equal to the length of the shortest path between two vertices, which is often greater than the Euclidean distance between two vertices. In addition, using an exhaustive approach always results in a lot of repetitive calculations. Based on this, this study designs a forward search algorithm and some optimization techniques to solve such problems. Finally, this study designs a large number of experiments on different real datasets to verify the effectiveness of the algorithms. The experimental results show that the algorithms described in this paper can quickly give the solution to the problem, and the efficiency of the algorithms greatly exceeds the existing algorithms.