Wireless sensor networks typically contain hundreds of sensors. The sensors collect data and relay it to sinks through single hop or multiple hop paths. Sink deployment significantly influences the performance of a network. Since the energy capacity of each sensor is limited, optimizing sink deployment and sensor-to-sink routing is crucial. In this paper, this problem is modeled as a mixed integer optimization problem. Then, a novel layer-based diffusion particle swarm optimization method is proposed to solve this large-scale optimization problem. In particular, two sensor-to-sink binding algorithms are combined as inner layer optimization to evaluate the fitness values of the solutions. Particle swarm optimization constitutes currently one of the most important routing algorithms. Its popularity has stimulated the emergence of various variants of swarm-inspired techniques, based in part on the concept of pairwise communication of numerous swarm members solving the optimization problem in hand. PSO is a population-based optimization method. PSO consists of a swarm of particles which move towards an optimal solution of the problem. Particle uses their position and velocities (P Best & G Best) to find an optimal route. This paper show better result of Maximizing lifetime of wireless sensor networking by using PSO routing Fully Informed Particle Swarm Optimization (FIPSO) and Diffusion PSO Performance of those algorithms is also evaluated over a set of benchmark instances Compared to existing methods that the sinks are selected from candidate positions, our method can achieve better performance since they can be placed freely within a geometrical plane. Several numerical examples are used to validate and demonstrate the performance of our method. The reported numerical results show that our method is superior to those existing. Furthermore, our method has good scalability which can be used to deploy a large-scale sensor network.