Moreover, some scholars added more factors in the model to realize more complex designs closed to actual projects [11; 12; 13]. In China, [PERSON] [14] took the drilling rate in meters per hour as the optimization object to establish a dynamic trajectory design optimization model, which could find the optimal drilling path with the minimum drilling time. [PERSON] [15] focused on complex underground pipelines and obstacles and presented an optimization method for avoiding pipelines and obstacles in trajectory design. [PERSON] [16] conducted research on the theory of the guide strength of the drilling path, which analyzed the contribution of the curved segment to the drilling path design. When it comes to artificial intelligence applications, an optimization algorithm is more intuitive and reliable than repetition tests and traditional human-computer interaction techniques [17]. The drilling path design and optimization were also developed further by several optimization algorithms. The catenary trajectory design method was introduced by [PERSON] [18; 19; 20], who tried to fit the five-segment trajectory with the catenary trajectory to design the drilling path geometrically. The genetic algorithm (GA) was used to realize the fitting of the two trajectories efficiently. In another study, the ant colony optimization (ACO) algorithm was used to optimize HDD alignment by [PERSON], et al. [21], which ensured the minimal drill path length and the reduced cost associated with it. Notably, the ACO optimization method added consideration of mechanical constraints (drilling mud pressure and pipe integrity) and geometric constraints (remaining in the construction domain) simultaneously in the design. The artificial intelligence algorithm is an excellent way to figure out a single or multi-dimensional nonlinear objective optimization problem which the design or optimization of trajectory can be transferred to. On the basis of previous studies, this paper tested a new numerical method, using improved radial movement optimization (IRMO) to preliminarily design and optimize the HDD trajectories and its parameters. According to the theory of catenary trajectory in previous literature, its shape is closed to the natural stress distribution of pipelines along the length so as to enable a lower drilling or pullback force during the process [18; 19; 20]. So, a five-segment trajectory was attempted to be realized by fitting with the catenary trajectory to get the largest similarity in shape. What is more, the drilling mud pressure is calculated in each iteration step simultaneously for consideration of the well-bore stability evaluation. The use of the IRMO algorithm can make the whole construction processing automatic with the intention to reduce the time-consuming manual adjustment. Section 2 introduces the concepts of the catenary trajectory design and five-segment trajectory design for HDD projects. Then we present the design method based on the geometrical constraints for the whole construction and each parameter. The objective functions for this method used in the IRMO algorithm are also summarized. Besides, the mechanical design is also introduced for the borehole stability consideration based on drilling mud pressure. Section 3 summarizes the framework and implementation of the IRMO algorithm applied to the trajectory design. Section 4 presents two design examples to compare and analyze the results determined by IRMO. The conclusion of this study is presented in Section 5.