Due to rapid global development of offshore wind, challenges of ensuring high efficiency in operations and maintenance (O&M) for offshore wind turbines become prominent in reducing the levelized cost of energy. Trimaran vessels, known for their high-performance characteristics including exceptional speed, seakeeping and spacious deck areas, give advantages in performing the O&M activities effectively. Current research on the hydrodynamic performance of the trimarans primarily focuses on conventional applications without considering their practical utility related to offshore wind O&M activities. When the trimarans approaching the wind turbine substructures, unique hydrodynamic characteristics of the vessels needs to be understood to enhance the efficiency and effectiveness of the maintenance operations. To date, for offshore wind O&M applications, there is no existing detailed hydrodynamic study concerning the optimization of the outrigger layout for the trimarans. Thus, a computationally efficient hydrodynamic optimization method based on Harmonic Polynomial Cell (HPC) method will be developed in this project. The developed method will then be validated against Computational Fluid Dynamic (CFD) simulations. By utilizing Nondominated Sorting Genetic Algorithm II (NSGA-II) as a multi-objective optimization algorithm in conjunction with Back Propagation (BP) Neural Network, an outrigger layout optimization method will be tailored for the trimaran O&M vessels under different operational conditions. During the fellowship, I will employ interdisciplinary approaches for my studies, such as the applications of the HPC method in calculating multi-body hydrodynamic problems, CFD simulations in validating the developed solver and the BP neural network method in improving the multi-objective optimization efficiency. This will effectively improve my research capability and facilitate the developed optimization solver for the applications in offshore wind O&M activities.