Many applications require the placement of sensors in an environment. For example, the placement of cameras in museums enables the monitoring of artworks and people in the museum space. In this case, it is necessary to determine the positions and orientations of the cameras. When determining sensor placement, one of the more important selection criteria is often the coverage of the environment by the sensors. This dissertation investigates the sensor placement problem with the goal of maximizing the sensor coverage of an indoor environment. In order for the computer program to be able to select and suggest sensor placement, it is necessary to model the observed environment and the sensors used. In this dissertation, an environment model with coverage estimation capability and models of sensing abilities for isotropic and directional sensors are proposed. The model of spatial sensor distribution in an indoor environment combining the proposed environment and sensor models is proposed. An experimental study on the spatial distribution of different number of isotropic and directional sensors is conducted. The sensors are placed in two-dimensional and three-dimensional models of the environment. Six selected optimization algorithms are compared with exhaustive search. Compared to the exhaustive search approach, the selected stochastic algorithms achieve significantly lower computation times with a small relative error of the environment coverage. The properties of the six selected optimization algorithms are studied and compared for the indoor sensor placement problem. Experimental verification shows that among the six selected algorithms, the Artificial Bee Colony algorithm achieves the highest average coverage for all test cases, with no significant differences in the execution time of the selected algorithms