Much of the United States' wind resource is located over deep water where fixed-bottom offshore wind turbines are cost-prohibitive. To capture this energy, floating offshore wind turbines are being developed. However, current design standards do not explicitly cover issues relating to floating offshore wind turbines, which leads to risk and uncertainty in the design process. Two important issues that this dissertation investigates are the effect of simulation length and wind and wave misalignment on fatigue and ultimate loads. Experience in the offshore floating oil and gas industry has recommended simulation lengths of 3-6 hours, but the wind industry typically simulates between 10 minutes and one hour. The reasons for these simulation lengths is explored and recommendations for floating offshore wind turbines are made. The current offshore wind turbine design standard states that co-aligned wind and waves are a conservative ``worst-case'' scenario for loads, but this assertion may only hold true for fixed-bottom offshore turbines. A large operational design-space set of simulations are run to determine the impact of wind and wave misalignment on floating offshore turbines. Using results from both the simulation length and wind/wave misalignment study, probabilistic methods are used to determine a minimum set of simulations that is able to accurately characterize the loads response of floating platforms. Reduction of avian impacts have long been an important concern for the installation of wind farms. There is large uncertainty in the impacts of offshore wind farms on seabirds in the United States, as no offshore wind farms are currently operating. In this dissertation, experience from Europe is used to create a model of seabirds' interaction with fixed-bottom offshore wind farms. This model is used in a multi-objective optimization of the layout of a generic fixed-bottom offshore wind farm, considering both impacts on birds as well as power production. To simulate a farm comprised of floating offshore wind turbines, uncertainty in the positions of the turbines in the farm is introduced, and the layout is once again subjected to a multi-objective optimization.