Location estimation has received wide attention due to the emerging demand for location-based services (LBSs) in indoor environments. Although positioning algorithms have been rapidly developed, the positioning accuracy has not reached the requirement of indoor LBSs. Indoor positioning methods based on the existing communication systems such as Wi-Fi or Bluetooth have the advantage of lower cost and higher penetration rates, which can provide a sufficient number of signal sources. Unlike the global positioning system where the satellites are well- deployed to provide four or more signal sources and well-conditioned geometric for outdoor devices, the critical limits of indoor positioning are the insufficient signal sources and disunified deployment for access points (APs). To address the problem, we propose an automatic hybrid AP deployment (AHAD) algorithm to provide optimal locations and required numbers of both WiFi APs and BLE APs for achieving higher location estimation accuracy. With the adoption of genetic algorithm, the AHAD scheme can maintain satisfactory Wi-Fi communication quality and fulfill user's budget for AP deployment. Furthermore, a hybrid indoor positioning (HIP) scheme is also proposed based on the combination of Wi-Fi fingerprinting and BLE proximity. Experimental results show that the proposed AHAD algorithm can provide better location estimation accuracy compared to conventional AP deployment based on user instinct.