Imagine yourself in an action movie scene: you are a thief inside a museum vault. You are there to steal as much valuable items as you can, but there is a problem - you only have a small knapsack. You want to choose items that will fit into your knapsack, but will also have the highest possible value. You can imagine another situation: you have \(n\) number of jobs that need to be done by \(n\) workers. For every job, every worker has a price which you need to pay in order for him to get that job done. You need to assign one job to every worker with the objective of minimizing the total cost. Generalized Assignment Problem is a generalization of these two problems. You are given \(n\) number of items and \(m\) number of knapsacks, with the value and size of each item depending on the knapsack you will put it in. Your goal is to maximize the total value by placing each item in one of the knapsacks, making sure none of the knapsacks is overfilled. This master thesis deals with exactly these problems. We will describe and analyze all three problems and give a few algorithms that solve them. We will also describe the connection between Linear Assignment Problem, Knapsack Problem and Generalized Assignment Problem. At the end we will present implementation of some algorithms that are discussed throughout the thesis.