Sensor technologies have become very important today in gathering information about close by environments and its use in wireless sensor networks (WSN) is getting widespread and becoming popular every day. These networks are characterized by a number of sensor nodes deployed in the field for the observation of some phenomena. The usage of WSN is ever increasing in the diverse fields of Agriculture, Underground water resource management, Traffic management, Crime surveillance, Dataveillance etc. Due to the limited battery capacity in sensor nodes, energy efficiency is a major and challenging problem in such power constrained networks. To extend the life time of wireless sensor networks as well as conserving its power, some energy parameters have been under extensive research, which play an important role in the reduction of power consumption. These parameters are as energy required for operation, communication energy and battery capacity penalty. They have a direct and indirect impact on the network’s lifetime. These parameters must be chosen in such a way that the network use its energy resources efficiently. In This thesis we have analyzed these parameters and developed algorithms for design optimization of wireless sensor network through evolutionary paradigm and fuzzy control. the design parameters optimized by the genetic algorithm include the status of sensor nodes (whether they are active or inactive), network clustering with the choice of appropriate cluster heads and finally the choice between two signal ranges for the simple sensor nodes. We showed that optimal sensor network designs suggested by the genetic algorithms can optimize battery consumption and increase network’s lifetime. The proposed algorithm characteristics and the impact of the chosen parameters have been investigated and illustrated in detail with various combinations. To achieve this goal, simulation algorithm that help in analyzing the effects of the parameters on sensor network lifetime has been designed and implemented in C++. Ultimately, results of extensive simulation studies are presented, and conclusions are drawn which can be helpful in guiding the wireless sensor network designer in optimally selecting the parameters proposed to improve the lifetime of the wireless sensor network.