Linear programming is the basis of quantitative methods that serve to optimize resources within certain constraints, enabling effective business decision-making. The main goal of linear programming is to maximize profits or minimize costs by using mathematical tools and methods to solve problems. The paper is structured in several parts dealing with the theoretical and practical aspects of this technique. In the theoretical part, the author describes the concept of linear programming, its historical development and key terms. In doing so, he looks back at the important stages in the development of linear programming, starting with the model of Wassily Leontief and Leonid Kantorovich, all the way to the simplex method developed in 1947 by George Dantzig. In the practical part of the paper, the focus is on two main methods of problem solving: the simplex method and the graphical method. The simplex method, as one of the key techniques for solving complex problems, stands out for its efficiency in working with many variables and constraints. This method uses an iterative process to find the optimal solution within defined constraints. The paper presents examples of the application of this method through software tools such as Microsoft Excel and the Solver plugin, where special emphasis is placed on solving transportation problems in order to minimize distribution costs between different locations. The graphical method, although simpler, is used to visually solve problems with a maximum of two variables. Despite its limitations in more complex problems, this method provides a useful framework for understanding the basic principles of optimization. The paper presents in detail an example of the application of the graphical method in Excel, where the problem of optimizing production with the aim of maximizing profit is solved. According to the author, linear programming is widely used in economics. Examples include production optimization, transportation cost reduction, inventory management, financial planning, and labor allocation. The author also provides examples from the real world, such as optimizing investments in hedge funds (example Bridgewater Associates), managing refinery processes in oil companies (example Royal Dutch Shell) and optimizing the supply chain in retail (example Walmart). In conclusion, the paper emphasizes the importance of linear programming in solving complex business and economic problems. The simplex method proved to be the most efficient for working with a large number of variables, while the graphical method remains useful for simpler, educational examples. Linear programming, as a mathematical optimization tool, enables managers and economists to make informed decisions based on quantitative data analysis, thereby increasing efficiency and profitability. Further advances in technology will further expand the application of these methods, making them evenmore significant in future economic environments. This paper contributes to a better understanding of the importance of optimization methods in business decision-making and highlights the key role of linear programming in practical solutions to economic problems.