In recent years, wireless sensor networks (WSNs) topics took advantages as it integrated in internet of things (IoT) applications, there are massive acceleration in using WSNs in monitoring, and tracing applications indoor, and outdoor, such as disaster managing, wildlife tracking, home, health, military, and industry monitoring. Several researchers worked on improving or optimizing one objective, to optimize (reduce) WSNs energy consumption, increase network coverage, or reliability. Since 2004, some researchers have optimized multiple parameters using multi-objective optimization algorithms such as evolutionary algorithms and particle swarm algorithm, some tend to reduce multi-objectives to single one by using weighted sum methods, others tend to make a tradeoff between multi-objectives and give number of solutions so that the decision maker can take a proper decision. In this comprehensive survey, we reviewed most of the researches that addressed multi-objective optimization methods for WSNs during the years 2004 to 2019. Some of these researches use existing algorithms to solve multi-objective optimization problem; others proposed new methods or modified existing algorithms, either with Quality of Service (QoS) or without QoS considerations. In addition, we analyzed these papers to extract the robust and weak points from them. Next, we analyzed the problems that these researches tried to solve, the multi-objectives that optimized, the technical tasks, the mechanisms, and algorithm that these research papers used. Our focus in this survey is to help the researchers finding the available approaches in order to motivate future researches go further.