Currently, a few online review and recommendation systems (such as Yelp and Trip Advisor) have attracted millions of users and are gaining increasing popularity. They usually rate and rank places and attractions based on subjective ratings provided by users. In this paper, we present design, implementation and evaluation of a mobile phone Sensing based Objective Ranking (SOR) system, which ranks a target place based on data collected via mobile phone sensing. Our system has the following desirable features: 1) it is easy to use, 2) its architecture is so scalable that various embedded and external sensors can be easily integrated into it, 3) an online scheduling algorithm is proposed and used to schedule sensing activities for coverage maximization, which has a constant approximation ratio of 1/2, 4) a personalizable ranking algorithm is developed and used to rank target places based on various sensor readings and user preferences. We validate and evaluate SOR via both field tests (using real hiking trails and coffee shops in Syracuse, NY as target places) and simulation. The field-testing results show that data collected and processed by SOR can well capture characteristics of target places, and personalizable rankings produced by SOR can well match user preferences. In addition, simulation results well justify effectiveness of the proposed scheduling algorithm.