Today, the need to use some kind of Intelligent Systems (IS) techniques from the Soft Computing / Computational Intelligence fields is rapidly growing. This is because intelligent processes have the capability to deal with problems relating user-experience-knowledge and also they can consider data being uncertain or vague. This research focuses on applying IS techniques to solve the Resource Constrained Scheduling Problem (RSCP) and some related problems. Traditional methods to solve the RSCP have proven to be useful; however, they are somewhat limited, time consuming, and not always effective. Among the traditional heuristic methods that have been used for years, the Serial method, the Parallel method, the Branch and Bound algorithm, and the Utility Index calculation, have played an important role in solving the RSCP. These methods focus on the scheduling of activities in a project and aim to do it in an effective way considering several factors such as, priority, availability of resources, and time frames. In this Thesis in order to improve computational effort, efficiency, and the option to consider different scenarios with each input being uncertain/vague; three new methods: the Intelligent Serial Scheduling, the Intelligent Parallel Scheduling, and the Intelligent Branching, are proposed to solve the RSCP. Additionally, two new methods: the Intelligent Utility Index, and the Intelligent Schedule Performance Indicator; are proposed to solve closely related problems to the RSCP. The proposed methodology for the solution of the RSCP and closely related problems is based on IS techniques, such as Fuzzy Inference Systems (FIS) from the Soft Computing / Computational Intelligence fields. These IS techniques lead to an effective scheduling process that allows to easily manipulate different and diverse parameters considered in the scheduling process. Furthermore, these techniques permit to consider each input/output to have diverse values/qualifiers (representing linguistic terms); and simultaneously, allow the user to include their knowledge and to use uncertain or vague data. In this Thesis the proposed methodology is described in detail, and different scenarios are analyzed in order to observe its reach, and to prove its functionality and effectiveness. Moreover, remarks, images, and charts are included to illustrate the approach and provide the reader with a better understanding.