This thesis considers the critical problem of the design and optimization of Water Distribution Systems (WDS). As the first step Genetic Algorithm (GA) as an evolutionary algorithm is used in order to design the optimal WDS and then Fuzzy reasoning is introduced to evaluate the penalty function for each potential solution. Optimal design of a WDS under multi loading condition is another point which is considered in this thesis. Calculating the fitness function for each flow distribution and then adding all the results leads to a design of a network which operates satisfactory in all loading conditions regarding to hydraulic criteria. In the last step, the optimal design of a network using Genetic-Fuzzy algorithm (GFA) is considered. The constrains are introduced individually by Fuzzy membership functions and maximizing operator is used to obtain a single number as penalty part of fitness function. The model is applied to two well known examples to benchmark the results. The first WDS has eight pipes arranged in two loops and is fed by gravity. In this problem, the decision variables are pipe diameters and the optimization problem is constrained by maximum and minimum pressures in nodes and maximum and minimum flow velocities in pipes. In the GA, one point crossover was found to be the best operator to reduce the convergence time and improve the fitness function. Anytown is the next WDS example which is a hypothetical urban community contains all hydraulic features of a real network. In this problem, the decision variables are pipe diameters, rehabilitation program, pump positions and their operational program, and tank positions and their characteristics. The present work includes a novel approach for the simulation of tanks as a network storage component within the GFA. In this way, a generalized fitness function that does not change with varying the dimensional characteristics, time and location is obtained. Fulfillment of the pressure and velocity constrains show the effectiveness of the GFA. Comparison of results shows that using GFA significantly increases the computational cost. Thus, it is necessary to limit the number of Fuzzy parameters within the system. Key words: Optimization, Genetic Algorithm, Fuzzy Logic, Water Distribution network.