Recently, a class of unmanned aerial vehicles (UAVs) called multi-rotors has gained significant attention. Despite remarkable progress in control and design of multirotors in the past decade, two issues, namely endurance and safety, still remain of main concerns. This thesis mainly aims at investigating about modeling and control of multi-rotor UAVs while focusing on safety, performance and optimal design. A complete model for forces and moments of a propeller in presence of freestream is presented which helps to derive mathematical models for two different types of multi-rotor UAVs: i) quadcopters with angled thrust vector; and ii) spinning multirotors with streamline-shape fuselage. Afterwards, equilibrium states and the constraints for both types of vehicles are introduced and using control design techniques, we develop ight control strategies to control attitude and position of the vehicle. The following control strategies are developed for: i) quadcopters with no rotor failures; ii) quadcopters with one rotor failure; and iii) spinning multi-rotors. Also, the performance of the proposed multi-rotor UAVs is investigated in three different topics: i) optimality of the hover solutions in terms of power consumption; ii) stability of the vehicle in different configurations; and iii) controller performance in trajectory tracking. First, this section leads to introducing six different configurations for quadcopters ranking from the most stable to the most maneuverable which are presented analytically for the first time. Second, a specific configuration for a quadcopter is introduced that leads to the minimum power consumption during a yaw-rate-resolved hovering after a rotor failure. Third, we present optimal design for spinning multi-rotors featuring minimum power consumption and best trajectory tracking performance. Furthermore, a framework for controlled emergency landing of a quadcopter, with a rotor failure and away from sensitive areas, is presented. Given a 3D representation of the environment, an optimal flight path towards a safe crash landing spot, while avoiding obstacles, is developed using RRT* algorithm. The cost function for determining the best landing spot consists of: (i) clearance from the obstacles; and (ii) distance from the landing spot. Finally, the framework is tested via nonlinear simulations and results are presented.