Scheduling, an important step in high-level synthesis, is essentially a searching process in the solution space. Due to the vastness of the solution space and the complexity of the imposed constraints, it is usually difficult to explore the solution space efficiently. In this paper, we present a random walk based perturbation method to explore the schedule space. The method works by limiting the search within a specifically defined sub-solution space (SSS), where schedules in the SSS can be found in polynomial time. Then, the SSS is repeatedly perturbed by using an N-dimension random walk so that better schedules can be searched in the new SSS. To improve the search efficiency, a guided perturbation strategy is presented that leads the random walk toward promising directions. Experiments on well-known benchmarks show that by controlling the number of perturbations, our method conveniently makes tradeoff between schedule quality and runtime. In reasonable runtime, the proposed method finds schedules of better quality than existing methods.