When RF-powered sensor network is applied to target detection, rational planning of sensor placement and charging/sensing schedule is an effective way to improve the system detection quality. Based on the fusion-based detection model, firstly, the joint optimization problem of sensor placement and scheduling problem is formulated to maximize the system detection quality. The problem is proved to be NP-complete. Then after analyzing the impact of fusion radius on the detection rate, a joint optimization greedy algorithm (JOGA) is proposed to solve the problem. Finally, the performance of the proposed JOGA is compared with those obtained by exhaustive search and two-stage greedy algorithm (TSGA), an algorithm that optimizes sensor placement and scheduling separately, through extensive numerical simulations as well as simulations based on real data traces collected from a vehicle detection experiment. Results show that, the proposed JOGA always outperforms TSGA in all the simulation scenarios, and is near optimal in small-scale networks.